{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weight Sampling Tutorial\n",
    "\n",
    "If you want to fine-tune one of the trained original SSD models on your own dataset, chances are that your dataset doesn't have the same number of classes as the trained model you're trying to fine-tune.\n",
    "\n",
    "This notebook explains a few options for how to deal with this situation. In particular, one solution is to sub-sample (or up-sample) the weight tensors of all the classification layers so that their shapes correspond to the number of classes in your dataset.\n",
    "\n",
    "This notebook explains how this is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Our example\n",
    "\n",
    "I'll use a concrete example to make the process clear, but of course the process explained here is the same for any dataset.\n",
    "\n",
    "Consider the following example. You have a dataset on road traffic objects. Let this dataset contain annotations for the following object classes of interest:\n",
    "\n",
    "`['car', 'truck', 'pedestrian', 'bicyclist', 'traffic_light', 'motorcycle', 'bus', 'stop_sign']`\n",
    "\n",
    "That is, your dataset contains annotations for 8 object classes.\n",
    "\n",
    "You would now like to train an SSD300 on this dataset. However, instead of going through all the trouble of training a new model from scratch, you would instead like to use the fully trained original SSD300 model that was trained on MS COCO and fine-tune it on your dataset.\n",
    "\n",
    "The problem is: The SSD300 that was trained on MS COCO predicts 80 different classes, but your dataset has only 8 classes. The weight tensors of the classification layers of the MS COCO model don't have the right shape for your model that is supposed to learn only 8 classes. Bummer.\n",
    "\n",
    "So what options do we have?\n",
    "\n",
    "### Option 1: Just ignore the fact that we need only 8 classes\n",
    "\n",
    "The maybe not so obvious but totally obvious option is: We could just ignore the fact that the trained MS COCO model predicts 80 different classes, but we only want to fine-tune it on 8 classes. We could simply map the 8 classes in our annotated dataset to any 8 indices out of the 80 that the MS COCO model predicts. The class IDs in our dataset could be indices 1-8, they could be the indices `[0, 3, 8, 1, 2, 10, 4, 6, 12]`, or any other 8 out of the 80. Whatever we would choose them to be. The point is that we would be training only 8 out of every 80 neurons that predict the class for a given box and the other 72 would simply not be trained. Nothing would happen to them, because the gradient for them would always be zero, because these indices don't appear in our dataset.\n",
    "\n",
    "This would work, and it wouldn't even be a terrible option. Since only 8 out of the 80 classes would get trained, the model might get gradually worse at predicting the other 72 clases, but we don't care about them anyway, at least not right now. And if we ever realize that we now want to predict more than 8 different classes, our model would be expandable in that sense. Any new class we want to add could just get any one of the remaining free indices as its ID. We wouldn't need to change anything about the model, it would just be a matter of having the dataset annotated accordingly.\n",
    "\n",
    "Still, in this example we don't want to take this route. We don't want to carry around the computational overhead of having overly complex classifier layers, 90 percent of which we don't use anyway, but still their whole output needs to be computed in every forward pass.\n",
    "\n",
    "So what else could we do instead?\n",
    "\n",
    "### Option 2: Just ignore those weights that are causing problems\n",
    "\n",
    "We could build a new SSD300 with 8 classes and load into it the weights of the MS COCO SSD300 for all layers except the classification layers. Would that work? Yes, that would work. The only conflict is with the weights of the classification layers, and we can avoid this conflict by simply ignoring them. While this solution would be easy, it has a significant downside: If we're not loading trained weights for the classification layers of our new SSD300 model, then they will be initialized randomly. We'd still benefit from the trained weights for all the other layers, but the classifier layers would need to be trained from scratch.\n",
    "\n",
    "Not the end of the world, but we like pre-trained stuff, because it saves us a lot of training time. So what else could we do?\n",
    "\n",
    "### Option 3: Sub-sample the weights that are causing problems\n",
    "\n",
    "Instead of throwing the problematic weights away like in option 2, we could also sub-sample them. If the weight tensors of the classification layers of the MS COCO model don't have the right shape for our new model, we'll just **make** them have the right shape. This way we can still benefit from the pre-trained weights in those classification layers. Seems much better than option 2.\n",
    "\n",
    "The great thing in this example is: MS COCO happens to contain all of the eight classes that we care about. So when we sub-sample the weight tensors of the classification layers, we won't just do so randomly. Instead, we'll pick exactly those elements from the tensor that are responsible for the classification of the 8 classes that we care about.\n",
    "\n",
    "However, even if the classes in your dataset were entirely different from the classes in any of the fully trained models, it would still make a lot of sense to use the weights of the fully trained model. Any trained weights are always a better starting point for the training than random initialization, even if your model will be trained on entirely different object classes.\n",
    "\n",
    "And of course, in case you happen to have the opposite problem, where your dataset has **more** classes than the trained model you would like to fine-tune, then you can simply do the same thing in the opposite direction: Instead of sub-sampling the classification layer weights, you would then **up-sample** them. Works just the same way as what we'll be doing below.\n",
    "\n",
    "Let's get to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import shutil\n",
    "\n",
    "from misc_utils.tensor_sampling_utils import sample_tensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the trained weights file and make a copy\n",
    "\n",
    "First, we'll load the HDF5 file that contains the trained weights that we need (the source file). In our case this is \"`VGG_coco_SSD_300x300_iter_400000.h5`\" (download link available in the README of this repo), which are the weights of the original SSD300 model that was trained on MS COCO.\n",
    "\n",
    "Then, we'll make a copy of that weights file. That copy will be our output file (the destination file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: Set the path for the source weights file you want to load.\n",
    "\n",
    "weights_source_path = '../../trained_weights/SSD/VGG_coco_SSD_300x300_iter_400000.h5'\n",
    "\n",
    "# TODO: Set the path and name for the destination weights file\n",
    "#       that you want to create.\n",
    "\n",
    "weights_destination_path = '../../trained_weights/SSD/VGG_coco_SSD_300x300_iter_400000_subsampled_8_classes.h5'\n",
    "\n",
    "# Make a copy of the weights file.\n",
    "shutil.copy(weights_source_path, weights_destination_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load both the source weights file and the copy we made.\n",
    "# We will load the original weights file in read-only mode so that we can't mess up anything.\n",
    "weights_source_file = h5py.File(weights_source_path, 'r')\n",
    "weights_destination_file = h5py.File(weights_destination_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Figure out which weight tensors we need to sub-sample\n",
    "\n",
    "Next, we need to figure out exactly which weight tensors we need to sub-sample. As mentioned above, the weights for all layers except the classification layers are fine, we don't need to change anything about those.\n",
    "\n",
    "So which are the classification layers in SSD300? Their names are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier_names = ['conv4_3_norm_mbox_conf',\n",
    "                    'fc7_mbox_conf',\n",
    "                    'conv6_2_mbox_conf',\n",
    "                    'conv7_2_mbox_conf',\n",
    "                    'conv8_2_mbox_conf',\n",
    "                    'conv9_2_mbox_conf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Figure out which slices to pick\n",
    "\n",
    "The following section is optional. I'll look at one classification layer and explain what we want to do, just for your understanding. If you don't care about that, just skip ahead to the next section.\n",
    "\n",
    "We know which weight tensors we want to sub-sample, but we still need to decide which (or at least how many) elements of those tensors we want to keep. Let's take a look at the first of the classifier layers, \"`conv4_3_norm_mbox_conf`\". Its two weight tensors, the kernel and the bias, have the following shapes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the 'conv4_3_norm_mbox_conf' weights:\n",
      "\n",
      "kernel:\t (3, 3, 512, 324)\n",
      "bias:\t (324,)\n"
     ]
    }
   ],
   "source": [
    "conv4_3_norm_mbox_conf_kernel = weights_source_file[classifier_names[0]][classifier_names[0]]['kernel:0']\n",
    "conv4_3_norm_mbox_conf_bias = weights_source_file[classifier_names[0]][classifier_names[0]]['bias:0']\n",
    "\n",
    "print(\"Shape of the '{}' weights:\".format(classifier_names[0]))\n",
    "print()\n",
    "print(\"kernel:\\t\", conv4_3_norm_mbox_conf_kernel.shape)\n",
    "print(\"bias:\\t\", conv4_3_norm_mbox_conf_bias.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the last axis has 324 elements. Why is that?\n",
    "\n",
    "- MS COCO has 80 classes, but the model also has one 'backgroud' class, so that makes 81 classes effectively.\n",
    "- The 'conv4_3_norm_mbox_loc' layer predicts 4 boxes for each spatial position, so the 'conv4_3_norm_mbox_conf' layer has to predict one of the 81 classes for each of those 4 boxes.\n",
    "\n",
    "That's why the last axis has 4 * 81 = 324 elements.\n",
    "\n",
    "So how many elements do we want in the last axis for this layer?\n",
    "\n",
    "Let's do the same calculation as above:\n",
    "\n",
    "- Our dataset has 8 classes, but our model will also have a 'background' class, so that makes 9 classes effectively.\n",
    "- We need to predict one of those 9 classes for each of the four boxes at each spatial position.\n",
    "\n",
    "That makes 4 * 9 = 36 elements.\n",
    "\n",
    "Now we know that we want to keep 36 elements in the last axis and leave all other axes unchanged. But which 36 elements out of the original 324 elements do we want?\n",
    "\n",
    "Should we just pick them randomly? If the object classes in our dataset had absolutely nothing to do with the classes in MS COCO, then choosing those 36 elements randomly would be fine (and the next section covers this case, too). But in our particular example case, choosing these elements randomly would be a waste. Since MS COCO happens to contain exactly the 8 classes that we need, instead of sub-sampling randomly, we'll just take exactly those elements that were trained to predict our 8 classes.\n",
    "\n",
    "Here are the indices of the 9 classes in MS COCO that we are interested in:\n",
    "\n",
    "`[0, 1, 2, 3, 4, 6, 8, 10, 12]`\n",
    "\n",
    "The indices above represent the following classes in the MS COCO datasets:\n",
    "\n",
    "`['background', 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign']`\n",
    "\n",
    "How did I find out those indices? I just looked them up in the annotations of the MS COCO dataset.\n",
    "\n",
    "While these are the classes we want, we don't want them in this order. In our dataset, the classes happen to be in the following order as stated at the top of this notebook:\n",
    "\n",
    "`['background', 'car', 'truck', 'pedestrian', 'bicyclist', 'traffic_light', 'motorcycle', 'bus', 'stop_sign']`\n",
    "\n",
    "For example, '`traffic_light`' is class ID 5 in our dataset but class ID 10 in the SSD300 MS COCO model. So the order in which I actually want to pick the 9 indices above is this:\n",
    "\n",
    "`[0, 3, 8, 1, 2, 10, 4, 6, 12]`\n",
    "\n",
    "So out of every 81 in the 324 elements, I want to pick the 9 elements above. This gives us the following 36 indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 3, 8, 1, 2, 10, 4, 6, 12, 81, 84, 89, 82, 83, 91, 85, 87, 93, 162, 165, 170, 163, 164, 172, 166, 168, 174, 243, 246, 251, 244, 245, 253, 247, 249, 255]\n"
     ]
    }
   ],
   "source": [
    "n_classes_source = 81\n",
    "classes_of_interest = [0, 3, 8, 1, 2, 10, 4, 6, 12]\n",
    "\n",
    "subsampling_indices = []\n",
    "for i in range(int(324/n_classes_source)):\n",
    "    indices = np.array(classes_of_interest) + i * n_classes_source\n",
    "    subsampling_indices.append(indices)\n",
    "subsampling_indices = list(np.concatenate(subsampling_indices))\n",
    "\n",
    "print(subsampling_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the indices of the 36 elements that we want to pick from both the bias vector and from the last axis of the kernel tensor.\n",
    "\n",
    "This was the detailed example for the '`conv4_3_norm_mbox_conf`' layer. And of course we haven't actually sub-sampled the weights for this layer yet, we have only figured out which elements we want to keep. The piece of code in the next section will perform the sub-sampling for all the classifier layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Sub-sample the classifier weights\n",
    "\n",
    "The code in this section iterates over all the classifier layers of the source weights file and performs the following steps for each classifier layer:\n",
    "\n",
    "1. Get the kernel and bias tensors from the source weights file.\n",
    "2. Compute the sub-sampling indices for the last axis. The first three axes of the kernel remain unchanged.\n",
    "3. Overwrite the corresponding kernel and bias tensors in the destination weights file with our newly created sub-sampled kernel and bias tensors.\n",
    "\n",
    "The second step does what was explained in the previous section.\n",
    "\n",
    "In case you want to **up-sample** the last axis rather than sub-sample it, simply set the `classes_of_interest` variable below to the length you want it to have. The added elements will be initialized either randomly or optionally with zeros. Check out the documentation of `sample_tensors()` for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: Set the number of classes in the source weights file. Note that this number must include\n",
    "#       the background class, so for MS COCO's 80 classes, this must be 80 + 1 = 81.\n",
    "n_classes_source = 81\n",
    "# TODO: Set the indices of the classes that you want to pick for the sub-sampled weight tensors.\n",
    "#       In case you would like to just randomly sample a certain number of classes, you can just set\n",
    "#       `classes_of_interest` to an integer instead of the list below. Either way, don't forget to\n",
    "#       include the background class. That is, if you set an integer, and you want `n` positive classes,\n",
    "#       then you must set `classes_of_interest = n + 1`.\n",
    "classes_of_interest = [0, 3, 8, 1, 2, 10, 4, 6, 12]\n",
    "# classes_of_interest = 9 # Uncomment this in case you want to just randomly sub-sample the last axis instead of providing a list of indices.\n",
    "\n",
    "for name in classifier_names:\n",
    "    # Get the trained weights for this layer from the source HDF5 weights file.\n",
    "    kernel = weights_source_file[name][name]['kernel:0'].value\n",
    "    bias = weights_source_file[name][name]['bias:0'].value\n",
    "\n",
    "    # Get the shape of the kernel. We're interested in sub-sampling\n",
    "    # the last dimension, 'o'.\n",
    "    height, width, in_channels, out_channels = kernel.shape\n",
    "    \n",
    "    # Compute the indices of the elements we want to sub-sample.\n",
    "    # Keep in mind that each classification predictor layer predicts multiple\n",
    "    # bounding boxes for every spatial location, so we want to sub-sample\n",
    "    # the relevant classes for each of these boxes.\n",
    "    if isinstance(classes_of_interest, (list, tuple)):\n",
    "        subsampling_indices = []\n",
    "        for i in range(int(out_channels/n_classes_source)):\n",
    "            indices = np.array(classes_of_interest) + i * n_classes_source\n",
    "            subsampling_indices.append(indices)\n",
    "        subsampling_indices = list(np.concatenate(subsampling_indices))\n",
    "    elif isinstance(classes_of_interest, int):\n",
    "        subsampling_indices = int(classes_of_interest * (out_channels/n_classes_source))\n",
    "    else:\n",
    "        raise ValueError(\"`classes_of_interest` must be either an integer or a list/tuple.\")\n",
    "    \n",
    "    # Sub-sample the kernel and bias.\n",
    "    # The `sample_tensors()` function used below provides extensive\n",
    "    # documentation, so don't hesitate to read it if you want to know\n",
    "    # what exactly is going on here.\n",
    "    new_kernel, new_bias = sample_tensors(weights_list=[kernel, bias],\n",
    "                                          sampling_instructions=[height, width, in_channels, subsampling_indices],\n",
    "                                          axes=[[3]], # The one bias dimension corresponds to the last kernel dimension.\n",
    "                                          init=['gaussian', 'zeros'],\n",
    "                                          mean=0.0,\n",
    "                                          stddev=0.005)\n",
    "    \n",
    "    # Delete the old weights from the destination file.\n",
    "    del weights_destination_file[name][name]['kernel:0']\n",
    "    del weights_destination_file[name][name]['bias:0']\n",
    "    # Create new datasets for the sub-sampled weights.\n",
    "    weights_destination_file[name][name].create_dataset(name='kernel:0', data=new_kernel)\n",
    "    weights_destination_file[name][name].create_dataset(name='bias:0', data=new_bias)\n",
    "\n",
    "# Make sure all data is written to our output file before this sub-routine exits.\n",
    "weights_destination_file.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, we're done.\n",
    "\n",
    "Let's just quickly inspect the shapes of the weights of the '`conv4_3_norm_mbox_conf`' layer in the destination weights file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the 'conv4_3_norm_mbox_conf' weights:\n",
      "\n",
      "kernel:\t (3, 3, 512, 36)\n",
      "bias:\t (36,)\n"
     ]
    }
   ],
   "source": [
    "conv4_3_norm_mbox_conf_kernel = weights_destination_file[classifier_names[0]][classifier_names[0]]['kernel:0']\n",
    "conv4_3_norm_mbox_conf_bias = weights_destination_file[classifier_names[0]][classifier_names[0]]['bias:0']\n",
    "\n",
    "print(\"Shape of the '{}' weights:\".format(classifier_names[0]))\n",
    "print()\n",
    "print(\"kernel:\\t\", conv4_3_norm_mbox_conf_kernel.shape)\n",
    "print(\"bias:\\t\", conv4_3_norm_mbox_conf_bias.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! Exactly what we wanted, 36 elements in the last axis. Now the weights are compatible with our new SSD300 model that predicts 8 positive classes.\n",
    "\n",
    "This is the end of the relevant part of this tutorial, but we can do one more thing and verify that the sub-sampled weights actually work. Let's do that in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Verify that our sub-sampled weights actually work\n",
    "\n",
    "In our example case above we sub-sampled the fully trained weights of the SSD300 model trained on MS COCO from 80 classes to just the 8 classes that we needed.\n",
    "\n",
    "We can now create a new SSD300 with 8 classes, load our sub-sampled weights into it, and see how the model performs on a few test images that contain objects for some of those 8 classes. Let's do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import Adam\n",
    "from keras import backend as K\n",
    "from keras.models import load_model\n",
    "\n",
    "from models.keras_ssd300 import ssd_300\n",
    "from keras_loss_function.keras_ssd_loss import SSDLoss\n",
    "from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes\n",
    "from keras_layers.keras_layer_DecodeDetections import DecodeDetections\n",
    "from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast\n",
    "from keras_layers.keras_layer_L2Normalization import L2Normalization\n",
    "\n",
    "from data_generator.object_detection_2d_data_generator import DataGenerator\n",
    "from data_generator.object_detection_2d_photometric_ops import ConvertTo3Channels\n",
    "from data_generator.object_detection_2d_patch_sampling_ops import RandomMaxCropFixedAR\n",
    "from data_generator.object_detection_2d_geometric_ops import Resize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Set the parameters for the model.\n",
    "\n",
    "As always, set the parameters for the model. We're going to set the configuration for the SSD300 MS COCO model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_height = 300 # Height of the input images\n",
    "img_width = 300 # Width of the input images\n",
    "img_channels = 3 # Number of color channels of the input images\n",
    "subtract_mean = [123, 117, 104] # The per-channel mean of the images in the dataset\n",
    "swap_channels = [2, 1, 0] # The color channel order in the original SSD is BGR, so we should set this to `True`, but weirdly the results are better without swapping.\n",
    "# TODO: Set the number of classes.\n",
    "n_classes = 8 # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
    "scales = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05] # The anchor box scaling factors used in the original SSD300 for the MS COCO datasets.\n",
    "# scales = [0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05] # The anchor box scaling factors used in the original SSD300 for the Pascal VOC datasets.\n",
    "aspect_ratios = [[1.0, 2.0, 0.5],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5],\n",
    "                 [1.0, 2.0, 0.5]] # The anchor box aspect ratios used in the original SSD300; the order matters\n",
    "two_boxes_for_ar1 = True\n",
    "steps = [8, 16, 32, 64, 100, 300] # The space between two adjacent anchor box center points for each predictor layer.\n",
    "offsets = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # The offsets of the first anchor box center points from the top and left borders of the image as a fraction of the step size for each predictor layer.\n",
    "clip_boxes = False # Whether or not you want to limit the anchor boxes to lie entirely within the image boundaries\n",
    "variances = [0.1, 0.1, 0.2, 0.2] # The variances by which the encoded target coordinates are scaled as in the original implementation\n",
    "normalize_coords = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Build the model\n",
    "\n",
    "Build the model and load our newly created, sub-sampled weights into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1: Build the Keras model\n",
    "\n",
    "K.clear_session() # Clear previous models from memory.\n",
    "\n",
    "model = ssd_300(image_size=(img_height, img_width, img_channels),\n",
    "                n_classes=n_classes,\n",
    "                mode='inference',\n",
    "                l2_regularization=0.0005,\n",
    "                scales=scales,\n",
    "                aspect_ratios_per_layer=aspect_ratios,\n",
    "                two_boxes_for_ar1=two_boxes_for_ar1,\n",
    "                steps=steps,\n",
    "                offsets=offsets,\n",
    "                clip_boxes=clip_boxes,\n",
    "                variances=variances,\n",
    "                normalize_coords=normalize_coords,\n",
    "                subtract_mean=subtract_mean,\n",
    "                divide_by_stddev=None,\n",
    "                swap_channels=swap_channels,\n",
    "                confidence_thresh=0.5,\n",
    "                iou_threshold=0.45,\n",
    "                top_k=200,\n",
    "                nms_max_output_size=400,\n",
    "                return_predictor_sizes=False)\n",
    "\n",
    "print(\"Model built.\")\n",
    "\n",
    "# 2: Load the sub-sampled weights into the model.\n",
    "\n",
    "# Load the weights that we've just created via sub-sampling.\n",
    "weights_path = weights_destination_path\n",
    "\n",
    "model.load_weights(weights_path, by_name=True)\n",
    "\n",
    "print(\"Weights file loaded:\", weights_path)\n",
    "\n",
    "# 3: Instantiate an Adam optimizer and the SSD loss function and compile the model.\n",
    "\n",
    "adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "\n",
    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
    "\n",
    "model.compile(optimizer=adam, loss=ssd_loss.compute_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. Load some images to test our model on\n",
    "\n",
    "We sub-sampled some of the road traffic categories from the trained SSD300 MS COCO weights, so let's try out our model on a few road traffic images. The Udacity road traffic dataset linked to in the `ssd7_training.ipynb` notebook lends itself to this task. Let's instantiate a `DataGenerator` and load the Udacity dataset. Everything here is preset already, but if you'd like to learn more about the data generator and its capabilities, take a look at the detailed tutorial in [this](https://github.com/pierluigiferrari/data_generator_object_detection_2d) repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images in the dataset: 22241\n"
     ]
    }
   ],
   "source": [
    "dataset = DataGenerator()\n",
    "\n",
    "# TODO: Set the paths to your dataset here.\n",
    "images_path = '../../datasets/Udacity_Driving/driving_dataset_consolidated_small/'\n",
    "labels_path = '../../datasets/Udacity_Driving/driving_dataset_consolidated_small/labels.csv'\n",
    "\n",
    "dataset.parse_csv(images_dir=images_path,\n",
    "                  labels_filename=labels_path,\n",
    "                  input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'], # This is the order of the first six columns in the CSV file that contains the labels for your dataset. If your labels are in XML format, maybe the XML parser will be helpful, check the documentation.\n",
    "                  include_classes='all',\n",
    "                  random_sample=False)\n",
    "\n",
    "print(\"Number of images in the dataset:\", dataset.get_dataset_size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the batch generator generates images of size `(300, 300)`. We'll first randomly crop the largest possible patch with aspect ratio 1.0 and then resize to `(300, 300)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convert_to_3_channels = ConvertTo3Channels()\n",
    "random_max_crop = RandomMaxCropFixedAR(patch_aspect_ratio=img_width/img_height)\n",
    "resize = Resize(height=img_height, width=img_width)\n",
    "\n",
    "generator = dataset.generate(batch_size=1,\n",
    "                             shuffle=True,\n",
    "                             transformations=[convert_to_3_channels,\n",
    "                                              random_max_crop,\n",
    "                                              resize],\n",
    "                             returns={'processed_images',\n",
    "                                      'processed_labels',\n",
    "                                      'filenames'},\n",
    "                             keep_images_without_gt=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: ../../datasets/Udacity_Driving/driving_dataset_consolidated_small/1479505696943867867.jpg\n",
      "\n",
      "Ground truth boxes:\n",
      "\n",
      "[[  1   0 148  37 173]\n",
      " [  1  40 139  86 172]\n",
      " [  1  79 143  95 158]\n",
      " [  1 128 143 144 154]\n",
      " [  1 149 111 256 210]]\n"
     ]
    }
   ],
   "source": [
    "# Generate samples\n",
    "\n",
    "batch_images, batch_labels, batch_filenames = next(generator)\n",
    "\n",
    "i = 0 # Which batch item to look at\n",
    "\n",
    "print(\"Image:\", batch_filenames[i])\n",
    "print()\n",
    "print(\"Ground truth boxes:\\n\")\n",
    "print(batch_labels[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4. Make predictions and visualize them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make a prediction\n",
    "\n",
    "y_pred = model.predict(batch_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted boxes:\n",
      "\n",
      "   class   conf xmin   ymin   xmax   ymax\n",
      "[[  1.     0.95  40.68 137.04  87.31 167.75]\n",
      " [  1.     0.81   0.43 148.85  35.93 172.36]\n",
      " [  2.     0.8  148.55 113.82 259.65 209.92]\n",
      " [  5.     0.31  75.24  24.65  85.85  52.44]]\n"
     ]
    }
   ],
   "source": [
    "# Decode the raw prediction.\n",
    "\n",
    "i = 0\n",
    "\n",
    "confidence_threshold = 0.5\n",
    "\n",
    "y_pred_thresh = [y_pred[k][y_pred[k,:,1] > confidence_threshold] for k in range(y_pred.shape[0])]\n",
    "\n",
    "np.set_printoptions(precision=2, suppress=True, linewidth=90)\n",
    "print(\"Predicted boxes:\\n\")\n",
    "print('    class    conf  xmin    ymin    xmax    ymax')\n",
    "print(y_pred_thresh[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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N91IVK1+H3ZV7VrXTV/QFO72CqT32FOGyiyGMbllb6oy43B/fe2pSg+F0depLvR1UDp9r\nW1GV0t+mnB/cwGfSF8mwLqurDK5CoVAoFAqF4oqCLnAVCoVCoVAoFFcUFkZFAZh4qe9QVzF5x9Hu\ntekGSk3uFtZx/l615VTW7um9ya5G+cOSesLbthu7dSEGJy7mbdtUjZQaaavyF5Xwq9o0u5utcuX+\nZu/YfKool78Ngr0y1vGN/yxO6WdBwUtf0MCFvFXCsAjsyQJo6BgsjnlYQ++FGkXIV9xrNBVanXUz\n3arD3GpNq296KbOoLFSVXtYWE9Ldk8I8Vzwe4yAj7fC2peY3fMZmBrPhmGbw7RoxhuRxsQjvIIVC\noVAoFAqFojEsDoM7TrAz3gIA9DpZs8YpyUwTFpVaCUkKLZadxhhyykyWStABAETcvQlruEsZCRc/\nsjxVp7zUH6U71IaY6h2AjjuTrE3tuMttowJESkpYvpt4nKUXmOiAEMRJwcUH/faDxBJhcMsD4xWF\noXy7fRKclCcSZmfSntoSYWiNpzJuv3HPkgsAMcq1My24Ey92XqTHjWgpl0J+7ZF3z7ljILVNrCZJ\n+UlCqWUmyG2WtG2rooKkyXMlkbHw3H7JMzZ9l0bz/LXTGpdreUdVVYYZ8xjcNBE1NdklDssn6U/D\nbvn/n2WMZwln4Brt1Nlx2O3gBzKPF4mxDEEZY5VWWEgFhTWX995MrXLaMuegGqZtpvDyTlkV7qPc\ncyZk6wzlNo6SQfQF0Zga/snninKW99EcfXbXCTbGUwr2Xc7eDzV2EKe5iJwl4EQDbxBlcBUKhUKh\nUCgUVxQWg8EdA5N1YG1lGUCePYv57y63dGtATNjOYBMAsH91FQCQWrJWfzAAAPRiyjQasPTI4srm\nuT4A4PBV3awiFkBGUY/SjC4BADotKiONsqEasWQhNQorN4nkvE8/lCXnzHdZIY3dDrt8gWRdcY6r\n4TJ7Wa6JuDMrcVcfe2tw3IPFWyZ18TqzjEyPF1gDb0AMV+ZKnPP862laL/KmzLPinnM20jHNr06S\n8WqiUy3c8oT/kO60+Hdikc2TxHHPIqIyTwBb6s4ChPA8NS7GkPu12+zq0JWygCEMk6f80kQ1UAhi\n0rA+8Ey62/mhnhuXyw2b23ebiZmmaxhVuBlqJuhIdf2hmD9Qb10WyN/3cQ2WdrdRpx7fHDe7jQ20\nNyiIRkA97pwr7ELNUU2a+9vPXldB3mGlIXW9rqyqywxxXxnS55C5HU2jTr2TJKByk91Z17jX5bPn\nsaVyqwmxi1A3YQqFQqFQKBSKZzUWgsHtbw/x8GdPortM3GR/uGOuHdxPv1cdvwoAsLZGTY46dOFi\nn9jYtqUA2UmICW7zYn+pQ7LCx//n/QCAn/mZnwEAvO1H32by3HTbccrT4TLAZfAQ2UKXYdYcJkSS\ntIyj6iyTSBKjSFhTv0zoDaMpNfKl9qRcD7Xs2JRvnY6iPLNalCxpTCNLVHfTDJNtbx4Ct1Pqcdqd\nkdnlXI20sUq4M0wqK8VKH+U3sTqdMej+8e8mVN9kMjTnxsL6mSxUSCehySKtH1sM7sAJyykRUH3C\ncVT4Q/TV8kxulTTqlisMXy0H6wFpLqdEHMrYLmKQhd2uzle8TNdpTK4Nd4xnDb9a1qb5+dS6bZi/\nlKafoXkQpMMqf1Q0phl94OmMqLxvffqtppwp9czS1pBdrzpz0b+b6YcvQEFdhDw7IWFyq7wxAGFs\nchWzWnbvC8xuwJC43qfmZd+BBVngKhSXG9944lacO/XI5W6GYgFw6NgN+P2TD1/uZigUCoViDizE\nAjdpJzhwdA2HDhKDO8aauRaTOi3u//zjAICT504CAE7ccAIAMEmINrvxxutNnhYv/VPWlewylXHx\nLLGND937BOXdybovknGf87RY91a45GXLM0LKSsIdcccgOpgOZzIa9U2eEefpdvJeB8RLQCaN+awy\n09wvYmKXcyStK9k4xfg4UslvhKwShwu2VajkiUyfl51MGccgIYVT55JLLlcJZUV2szxPVxLl1V2N\n7jUdCMvLbWMmlxa3zzS7b8Vu4Nyp+gzMPKTNPAxVFaaxfX4dzfyx+97w5dkthnMWHevdQFzDBLxp\nPfNi+eWob/s+H1pxeU0hOrGCJvxAF9nN2dKU5Qm5r1l43foTN2QMSse0wgdtxnj62d5ZQg/7dodd\nbwxFpnWGu2x7ACottxqqg6tQKBQKhUKhuKKgC1yFQqFQKBQKxRWFhVBRiJMIy4faxv+VvfNxgA13\nNsfXAgCGHASiu3YAABD16PixM5smz6XT5wEAK7yVv8ZGZ3/0oT8HAIzHlOfSRmZIJMz3DqtE7Ih7\nMjY6SyyjodGYDZH4lLg1EyOjbocudFq9rI+S11D19Js6qgn5rRDfOWBHyqhi6x1Dt5EvidO2sagk\nVOx1GddbfG0kRngV5QvGJRdS75HrwsxtQRFLslfqaHvYoSIiY/g2vTzFsxdlRqCFdMGhMCvS8QNX\nx9m7m9RvnCq/ZQYmISVzGRW7riGqBLMYq1XsgAdjMpnNOGVWNOGcvgqJZ5K4r70QpDPMORchIeld\nxbuQ74TAHcmqJzKoG9NcWVWUG+aqLG90XmdsTdCjCsO9gtvQGi6zyuZlyNfPrTfIzdkMfhibmJMu\n9OuuUOwhXvta4P77gdEIeM976NzLXgZ86lPAYAB86EPADTfQguIrvqKZOh96CHj727PjD30I+Lf/\ntl4Z73kP8Id/2Ex7FAqFQqHYbSwEgwukwGSMJBJDrGzdna7T7wGKwoq1m8md19kt4uUO7CczCGFl\nAeBwj6jgx+55DABwbmcDAHDs4GEAwPXHr6HzTz1t8nzsI/R7zXOvBgAMuLintyjvdftWTdp+n+re\nz3UbL1eTvEsr222UMKji9mpsZAtmQIVYtKQXv3o4MEYf0xCxeYht8oXCmbwbKpGchDnxGpRIXk6T\nGH5U+p611hX0pjnNzrczyR2FYJxRVvkSc6Q4M9sBot0f/iHw+OPA3/pbNRpRgTgGfumXgH/9r+nf\nBk0t/PzPA3ffDXz91wObm8DFi8Dx48DZs83U6+Jbv5UW2E3j7W8H3vAG4KabZst/663Az/4s8JVf\nCWxtAb/+68Bb3kJ/l+EtbwFe/3oSCuIYuO8+4Kd+CvgP/yFL85VfSele8AJK96M/Cvzzf17dlkaY\n2UAYN31zlOHjS7L3R3jJ5S7F+HoFMVPF2cxiMNYEg2uMYmvkcW99E2Gqqdz5O+QbkyJ7Vp5/Yu6j\nY2EseWuxjuWJTT0B5YRWWW28Nb0UdxfBzA3HcLqq9KCpMOV5rmI33W+yjcInK2A+me91gKGYuVYw\nNPUbqFXCads4gNENeQ+mQdFFMiiDq1DMiXZ7ehoAuPpqYN8+4Hd+B3jySeASBcvDrbdmi+nz50nl\n5dSp3VmEAlTH+vrulD0rVlaAD36Q+vzlXw687nXA130d8Iu/WJ3v4YeBv//3gRe9iBawv/zLlOeb\nvzlLs7oKfPazlO7kyV3thkKhUCgWBAvB4LYQ43Daw6Uz5wAAFwfZ13dtk1hXCY+7/yi5ELt4juit\nzXXqwsrKislzKKEQvI88TQztDcevo9+XvwwAcOvBgwCAF910o8nz0U99AgDw+L2fAgBES6zru0p6\ntPeczgIanL1AOr69Hl0TYWV5mXR9b76Zyj1+9VGT58AhDils9FzZlVjKDKgn8ICE2XMl/3Z7p5DW\nlciMVFohw0hYWmF73ZCxRifGEn3delocqjdjBLIpNTFBGvLhdrMS/CF86RznEcbVHLdK84xb49yx\nT+8vTvPjXMaqvOc9wCtfSX9/93fT71d/NS2oHn4Y+Ot/nf697GXExr71rcC73gW84hXAiRO0kPq1\nXwP+yT8h1YPXvx74d/+OyvmzP8vK++M/pr9/+Zfp33d/N517+GHgpS8FPvxhun7VVcA73gF8wzcA\n+/cDjzxCx6LmUAcf+hCpSbzxjXTc6wE//dPAt387La5/5VdoAfy619Hi28Yb30hM7cGD1M43vhF4\n+mnq34//OKWRqfBjP0b9D8F3fidw5Aj9ysL/zW8Gfvu3gR/5ERoPH97//vzxv/pXwN/4GzS2H/gA\nnfvd36V/AI1ZCEJZu1BSrlJdPqyIHAp6cTOU4cNU1qmSXgnXdw0b3ibY8ZJtHUxvpwlr25RSYANM\nsI/BdXtY5TEyC1VOidx3pGEqA7rsfll89cwDN1BJ1U5EmNs8d2vPPSzXezWBBgLqmbpjEtnfU/4t\nuQ9zo3Rg/Pc/pO5Z9P2TOXcvZnUlpwyuQuHB3/27wJ/+KfC+95G6wPHjwJ//eXb9He8A/uN/BJ73\nPOCd76R31tNP0wLtjjuAH/xBUm14GwfLe9/7gC/5Evr7m785K+84adzgzW+mv9/3vmJbej3gT/4E\nuOsuWlTfcQfwpjeROkMTeMc7gNe8hhaGL3kJqU686U3FdF/yJcDLXw78tb8GvPrVwPOfD/zLf5n1\n7yd+AnjssWy85No//sfV26YA6Rt/5CPZ4hYA/uAPSM0nVBc5iqhdt91Gi3iFQqFQPHuxEAzu+dNn\n8P6f/yWsD4jB3RhcMNc6l/YBAEYcOnXMOp8T/jV6o7G1Vuet3f4GMZ0fXyLWN2Ed2fGA8t53990m\ny9NnTgEALmxeBABsc5CGpE1DlLSygAbnzxODK4ztiPeSx8wOHjlyCEDG8AJAd5nDuiYk204m1IYh\n/07YFUOa5llIu4+CiTChVXo0zjXRgYssZThhKESXSsZQzkub5Jc6mT/XSqJcG/NsL5fj6P+6/fH1\n2WVXhMGN0M4d29hoha/4TItSv2x46RIxr9vbpC7g4hd+AXjve/PnfvRHs78feQR4znNoofhjPwbs\n7ACnT9O1c+eyMuX34kV/PQAtmm+6CbjlFuAJilFSymjWxfIy8H3fR+38rd+ic297Gy1kjxzJp+33\niWEesKeRd76TFvIA9W9jgxakbj/OnAHuuae6HVdfDTz1VP7caERjdfXV1Xmf9zxaHPd6wHBIwoL0\nZVbMyzgI5rHu9iaV990cbanDhmSsYFE/rrBrJFdLWClfW3YbIWzydCZ3QSJOwK+jWWarUeW5IOXv\ngPsWzXQ2w1pjY162zB3lpOS8D2VzOj9O9XXRXRuWEP1T4xEhoB4JA9+0/r1pSwWbD5QEgKoTlph/\ny+Zghvl2dUw7NVSvQrH7+Iu/KJ57wxvo3403kk5pq0WGT/Pii7+YdEhlcdskbrkF6HaB//E/8uc/\n8hHgm74pf+6ee7LFLUB6xMeOTa/j536O/u0W7r2X9G/37QO+9mvJyOzkSeD3fm/36lQoFArFYmMh\nFriXzl/E77//t7E1of3JzqolV2wQC2osNkUvlZm3jIXMEIk3A5G6Ro48lhZ1P2P+W9hEYSgHfJy0\nzpu0bdZdHbKljrCyUtzZ81la0yZOI2mH47wFkWFNLQFF0grGXO+4w3rJlhWSXDPjIWxsLOOVr4f+\nppP9PrHVvSXSXe61iW2+eJHY7EOHDpg8cs60N13mtralUJNW2ics+HhMLHxvlVxiDIek15xaXnpN\nMXxuxHmkvm6X2pjE2dQVJmnAYXhl3GRMJI+dth1TmovnivcqBK56wGtfS4u4t76V1AkuXQK+7duA\nf/EvZip+zxHittBe3EqeJhbwAC1Ir7suf67VAg4dmm4YNhwCDzxAf3/iE8DNN5NaxHwL3HKe02aC\nprEdIVxImb6irwWzkIlmd0XKqEydv6FFC/209FpBL3hO4nO3fcqaeqZM/hDSaBa/n3Xg2kdUNamK\noU/zn9HC+arHucn7W0utOaCeMo8bIbeljh5qiDcCd97KvfM1pfTZL9lZBOqGxg57QVc91yaNz6Yl\nsB0hXhR2A6qDq1CUYDAAkkBria/6KuDjHycjp7/8SzLiuvHGZtrxsY8Bd94JXHNNM+XZuP9+Uj34\nsi/Ln3/JS+qXVWe8XHz4w9SGffuyc696FZUnhnahiGNSV1AoFArFsxcLweAiThAvHUQyYubN8iiw\ndIwUAVtskd9mKaLDDF6HmTjbV5uwl2LFf/7CJT6m6+0OMXr7VjLftitLxCp2WLezk1D5wmaePPuY\nSSvSjjCDa2uk47t/P3lnWGLdXFsHt92j8oVVFBZ5xDq4+/jL3mplPqe2dnZy/RGssR/fycTD4HLb\ner1Oro2ydksrAAAgAElEQVSSdqefeYMQhlUY2n37VnL9O3vudKEfw+GQf4n1vXSJBvX8RdKbPnz4\nKpO2K14mmEU+eOQgt4nGdnU/M8ZLWZ/HE2pfi2+09COJ82229YJlbrRSGmNXlzhJsrkx3GGGfkS/\nH/kTcmnw9/+nY44PCpDw8pcTI3jxIv0rw733At/zPWRA9ulPA9/4jeRvtgn86q+Si6sPfIB+H3iA\n2nTkCPCf/tN8ZW9tkT7xj/846c5+/vPkEeHOO8lorg4eeoiMy17yEvJHu7VFOsxvfjPwAz9AxnFl\neO97gX/4D+n37W8n5vbnfo48UYi+8YkT5ErsR34E+M3fpHM/+ZPAb/wGGbetrJCXie/+bhonwcoK\nqWIAQKdDbbzrLtIZFubXRUiUJqCeXl9pGSVFzGuJXk+fUuBnWqq8KMi1OmzJLJHNLhdiz50o1T+u\nQFWUqumZuR7P7XGZNfc+2Fxg5l0nbztR6RyDf91RSJxMdXS7q3S5zXlpW9Bw+RNV5S3rlzftFG8E\nNsrmdp277nv/yHjUeS8U7klJP7yeEaTTcVH/vpBmClxfulXwlWmi9dV8dpTBVShK8JM/SQZSf/VX\n9Ftlzf8Lv0Buvt7zHmJyX/xiMi5rAtvb5I7s05+mBd/nPkeLP5bJ5sY/+AdklPXe95Ju8aFD5NJs\np+iNrhK/+ZvAf/7P5NrrzJlskXnkCHD77dV5NzfJLVunQ/q/v/7r5EXhe74nS9NuUzn792fnTpwg\nt2b33kuqId/yLbRA/6mfytK86EWkuvCJT1D6H/gB+vvd767XP4VCoVA8cxDttu5QCLqrR9Jrn/ca\nbI/YR1DHYnCXlcFVBncPGNy/+Uo04qjyCsEHP0gBIV772svdksuBCJ+q0IFrGrvFMjTZg6o48bN8\nQ55JDG4UwOCGYC4Gl5EEMLguQnRyq1DW07bDys0636YxuGGoP6Z17mCd293E3Pbq9s5Q7qSgMzxD\nGypCCoY+B2nDDO7Lo+hjaZq+aFpZC6GicO0NN+L/ffe70V2hxtsqCuMeL174uMcd7cpitUI5Xlxw\nnLlEi7ItXpSNOKZrK7ZeXGyINljnwAV9yiML6DODLDyuLPImkIUuLayWZJHc5oVdkrUq5nLiqMXV\niUsxOi9hW5ezeBU4ynEixGaN19HgNTE6HauvfG6nL/XRr+g0dngsbNO2DQ6BatlgAcj0KPuDawEA\ng0E24TodMWLj9vKNkTG2J2fCwsGIF6NtDsF85jwF6di/nxevuVnY4/7wQnpCi/yIrc+WOnS9jWxR\nbLaaNvN9jz2zO+lQ5w6s0O91N07xQfUswPOeB7zwhcScdjrkD/cVr6BIYs9W5IxWKx3Mzy8UhapD\nVKGOAUsVShcpnq1ss2AraX/VBzmkbXsnYlTDbqsZ51kWqU24nvN8/MsMxgS+b2M6LZysXX5g00IE\ntTr3tInnwoYbjKBO6cbGLyBt3EBgkIkv+NFMJfmDW1QtTLPFZEkZdnGlxn2uod30J16i8PoEwCxA\nST2oioJC8QzGz/88RR3z/fv0p8PKSFPg+78fuPtuWuS+4hW01f/7v7+7bVcoFAqFYrewEAxuuwdc\ndUtkJIduN1t3S2AjaWjbOZYtG1uKNVIiF3P4COVatVg/IC+ZS3mTEVGo8ZDr4zYdtUaKvWqZX9kt\nlybIseXFS+Ij4MIlYiSfZguejS4xiBtbdH54YWjyXPgM9f6Tn/wkAOA69qP0ZV9I4a9WVzMVi05X\nXHERldtqU18HrI7Bh7k2tZi53WL2t8V9lFEShnhgBYcYcC/bLRrchANu9Nbo/KaluLnUo5I6fCPk\nvhw5RqoKw1RY2qxRyxxmucOqGoMJDfJExDu+U3ZoCMkt0ZpFyvO5RRLmfIvb1FpbiEdgZvyjf5RF\nDHMxHPrPu/jMZ4peFJ7taMJ4LLSceVx/ZfXMh6Jje//1fKW841bCBs3rJqxiZ3RPEVm9L2U890rV\nL2BMCq7dvG6wqtsbxrTW58e8O621S5kN0whJmzl0k9QK1Vsy8SsDMwWw7qHIGxWWWbyV92Qa12r3\no9Sgzik/iPl2yvKNV9338jP7665QPMtx+nQWIU2hUCgUCgVhIRa4UUQs7gikRDlAxgLGYMMnPhZf\n8yL1tURSyC3sKbUoWKcs0xScNFtyUspyS7vFOqatvAxluedEj0dtspJLUmAObflD/h6PSYf0ubdc\nz8d0XtjOvsWwPvwwXTx04DYAwApTlElC1Oq5c5nfqnUOOjFgb/xiGLb/ABmQSRAHW7IaMJ3bZnr3\n8BFS8j3FeshZAIssj6QV8nhrSGlaLfpdSTLF4IHYs7FIKLq9K6ybvDFmCjmyAjHks2A55nDIfDzm\nCdCyZu4q36r1EbW73WLdXw7SEUdZm2LWA5Z51F5Rh6kKDywjs0qjoKnMXYBh0SzqnAFpylpWzSTV\nb0xIWONZ9GkXhMANgnHmv1cBH+rk8ZyrMhoE9lZ3Uepy50hRj7O876U7D14W0F+fL02mZx5eftnW\nQ9UcCdntCFXttceijB33BX4qwwTl96GM7XX7WCs8eIMP/kIscBWKy43u8mH0t55Jn1TFbuHwsRsu\ndxMUCoVCMScWZIGbIsYAbZDS4A7WzZVVw+C6klRs/Z9nZzMJjcPtgvQ4OyxvSNqxJVckfK2FDqeh\n+kYps5zIFBrbxgLVL78Yy+rUkqUkhC67DFhmFlNK3RxRG1ctzwvHb72K+3EkXy732taxHOUj/xoV\nm4lzfWClW1+nArb7xJgP2JPDhdP0x9YWMaLGtRkyVnf/QdKjPdolp6QisD3yeLZf/pnPfAYAcOtz\nnwsAuPpa9ljAXjJOnKCyti1/q10mVFMeWrmXogbcFjbY1rnmTko7DzC9uyUeKzxcloxLh5nu1/zQ\nDwEATj/wKD70a78AAHjLP/tZAMDXfDk5wH3728ix69FjdF8++omPAgCGExrHlX3Lpvz2OnnUaPWI\nnR4z7f/Ip/8UAHCvJdJuc/MGMtfybiWQMJuYs+yXKSb6zTxQY2eNnlS4u3Kl93lZG3eHpKALWKus\nZlG0NK9gMRtk4UIYkjpO0AUFtsszurOMYToJd2WVBQlwd8aKbZk2t3yz9BlpAd2EpwQL7lxsamZG\nU7wohGDSmJ+LfKCQcjdbVa2dXx/YV/pkBmbe9f5QFRSkjEn3PX8uqxyCae+WkDs4rT9AQPtrTFw3\nGMmMxQB4hr5DFAqFQqFQKBSKMiwEgzsaAKcfTbF6nHRAO50sVNHyRNhWgkgKE2cpb0t9EuwgZqZw\nlXU8hZWV67YgMoEEB4hz5cdMJVouZwvSiStxeh16c3kScOHsNimorq1ynz0StdR5aZt0k1eXSPG1\nz4PRtu5eN+8gwnhtkH4IM2rrEh88QJk2NtnHLOsUX32cHPAOuJ7BAAWIV4Y+k+2s9oql9YzVXjvK\n/oH3t7kf5ND3/vs/DwB45EligVdWspBc+/dTC5fZp3DKHREGd8L+is+ePW/ynHryFADg+S+g8no3\nUN6IHeG2fJKgTKgxtXfzArHVI8vn70pnhc9RG9ZWDgEATp88Q+Wybm/KrPagbwWf4DkwHA3y9TEm\ntujMnUvi/I5AwpNGdBxt0tFI81HeF2nBg0QNRqk5zwG7k3ZansqyCl4HylnHsjHzjc8kRMe2JG9J\n04Lg7h2NQ0LFBrQ1rjVf3PLl/Ax6vAHlXy7skX8Ep07/vWqMwZ0yuiHMV2Pj0oBHkLINGS8LWGb5\n7zkn89LVTR/7vvFlbQtggc2OnJSbFO/AtOfXq9s7pXlxxU0M9ZAATGeX593YMDEnauZTBlehUCgU\nCoVCcUVhIRjcQR948qEUL7yGLdwtISBhPVMxTBS1VtE1FCnDlkRSFtHiCevVSl5m0URaSmzagNMM\nHZ+2bQkRCyfclw03WEhUPJ1m9AYAYGmF2D9DJKZ0bDPT0tceM7fbEsEs2eDys8QSCk/0iiMnhLEw\nTbZr1Db3SfzHCqkov2P2XWCH0l1mmXaTUy0dZDacz994Z8YR3/aFLwaQMc5CUN5210uoLXLeErMm\nZkD4WHSH2SPDKWZPH3niSZPnsUefAAC88uWk67vEzC0Tr7nyjYcLvtZihr6/QUxrN7Y8LkyoHAnv\ne+Yk+S4+fY7aMI7ZSwM7S5YodUA2ByLWB3Yj04xGFmPIYfkm3HkTathhZW1p1LU8zvQh8+dD+JCm\npNxpNU3zvRleT7Wubx3MwjraKeuwvb78TcHHgJq5YeotrznUz2dV5DZ392BeLAqDO29LfP64p6ct\nm1fNYhEYrum6+9NnVC2GUNjSAObYvXfGy4+nwib1pX33xQ27m9VTQ2++Thtk17TG2Ja1ocq3rvue\n8pcb3gYbizC/FQqFQqFQKBSKxqALXIVCoVAoFArFFYWFUFHodCJcc6JrXECNccpci1q0fz7hreMJ\nG/bIVvskpa3lTmRto/PeQyTqDexTatihfW6xmeojc+skDLi4n+pGZwEALdC29Caea9K6bjOMqzJR\nfahQwi7AcX9V5eTYpEmXy9OUZ+fyiy5KyrAq2/W5ZLShIGaAWXmy558U0rr7E6mjMW63IxV9EpmZ\nohnCahTXHSFDsi9+3gutEunvJKKKxBOaqJck46z8OKKrfR7MrQ61sZ9wYIxupqLQbbP6RY8s6bb3\nkbHfeIdcurUl6ASX30osBZCE4zinpLIRjzJDOgDoWTd6p0/5u11qW7aVT4OQpqzmYBtFGquK1Pqf\nctNxZJUwO6Y5IK8yUnCRVsRelWcoxBAjLhHLpS12EWVbWyNHpWNWzOLiy0UTW/q+VpgxdeurcH4/\nnfGoUHMQ1awaboyqtj/rzK3dxcI0xLxvQ7btSwqwf+aCz3h3Gnxuqdy+FLezq1RrCKM6LstKipvb\n6Znz6RJUPg+mLXHu2JfDGLyVlOc7P4iLxuv++sPhN2arNnyrek+WqS/43g11XTgqg6tQKBQKhUKh\nuKKwEAxuFKdIlofoj6k5SZLFwB0lHKq1xGF9NCbO0maHho5BWip5J8xvsjFQleLzGOymCsQYuqxt\nrg1iDFQhDTUZynGWcI0Cux9NSPFVUm+pyxDH8C0uo+Sqyq48G+WPrPGSv0WidMMRb2xsmLT33Xcf\nAODehz4OABhyZA1J2+MgDoMtYnY3N7dN3l6P5nDMYzCZhARcYGM1Mz7hiDh1Wjmrp5Xh+duwck7a\nGRjQkJlfNbenGkAtDtG20KgOPTxHudkDF5yncrYujpXZwmAeV2ycMV+QHM5QXNEYbDp8b/qyfCHv\nP7Pz2shOyu68QOb5XvtaVHAdV+GyLG4gGEeV0d/UgCHiDMD16xpSb27HMl9fKJTBVSgUCoVCoVBc\nUVgIBjdpRzhwvIMui/MXJtm6e4nZPQlJKjqybaM0VqSSxIXYuCXSi7jMSqwjSMRYLt/JC9LJHPFv\nYskCxRB/fmf7PlljukP4Ypuq0oRit5zOuAxMrs1Rnqk1aSQQh2Esi3LWeJo07RlHCR3pjn/kUSSV\nuttxnsFdXV01aSTU8IUtckn2tre9DQDwM//ypwEA5x8n/ewOuwLrLmV5h+xvLoSxl67IHMv6kd8Z\nyDOsri5dvtcJinmmtiOgjS58elLT9DntLHUk7LI2yBBfDsKviTp3u911yp8aUjegMF+SWbixRSFw\nF2ljoM7+TKX7JRNZIModVqHgVsvd1amRN5/Pf9Gdi1VNbCJo8F4F0fAzxfndx5C579uhLE/VHHI7\noqa9/nZL7RXmF0EtNPO+ZneUwVUoFAqFQqFQXFFYCAY3AnkIkOABnTjzEjCxEwGIeQnfdfQUh9bK\nfszU7BBkyZ6wKX6L1/Psnx+JFT41ZZPdiFnfEcsMEvK27REFIuevvZIWqqTteULizWK17NbnIywn\nRv/Y5MqXYec356r1jMq0e31Htt6xjJ0w9S1mX1OOLLG9nenR3nXXXQCAm277WgDA+9//fgDAuXPn\npFG5fomOLgAkCXljiFnHd1wYqGLrRbKXX+Odw9u7/Lkyh+T2nNxrJqqMVUk9f+81W7co7OAzDSHv\niCpW/5mIRWq7n/0LY0C9eURHcoa21BqXinkzLr8UXN+8oWCn1xCO0P74Uc7kukNYok6dQzLDqmSa\nLrK9K+nuLpaNoO/+zBIApa6dhTK4CoVCoVAoFIorCgvB4E4wwc5kE0lMluc+a27Rc20bdosZXAm/\nG2XsWRT3OY3oMrKnBdHtFUepljSQxMTciZ5uLFofFfp9LpuRSSSub1KrPzVEzVIJrYLClUu16pmD\n2ktCGB1TUf58lVQm54z1pOMgMDdHCnqoThtzdeY108SBQ8pUfSvOUnc6xMLedtttAIBf+tV/DwDo\n92l+LSc0ryZjmlC2nlmbKX/xEFEWZhEAkpL2F3S6rcsmjKKj/10lFZfpR/nHP38yrlKiKinXRZU0\nXdbukFrNHavS83P1BxdIs3K3LcDrWdyHj0uZXqWyJrsHP9Na31rDzTHLPXO/f3VCutooW4TUeULN\nW30eLyANUfVuE9xnM6yJHl+zzv2s42Vieunl9WR5yr01lJWb7UYW84Qw0G7Jde+RvosUCoVCoVAo\nFFcUdIGrUCgUCoVCobiisBAqCjEidOOu2WocW1raLbYEM9up/DtIeVuYw6O24oyw7/C6fWS6x9vF\n4o5J4j2kW1YjKO6qGKQZ/p/VGaIKUSBjzYsE/XRM59xNCi4uRCpxjbJ8aghOfIHZjMyqrhnDQG5T\nSbqR51zkdrqyDbI9L6oheXWGxGrkmMPqRtzZRFQIOBDDpUuXTNqdzS1uCxVw+PBhAMBtN1HY5o//\n2UcBAC0zzTwmXeKLLsAwMDIjJQ/AJHfed3/iXMpqIzMX2TNVLFjOZduOe2tqE1Jb2Xz1bWMVjSCa\n6U8TzuGbcTXW0P2ZQYVqHlRvlSpc+F2wlY1i+ehWqUyFwtVamrXEMrWCOioPId+uaeoLNbSwKlF0\ncemoe1WMVLXaQXg508orqKtVhUg3wRyi0rSlr40ASzLzzapqg9OWUCiDq1AoFAqFQqG4orAQDG6E\nCF20MB4Rc7WcZIY+I+b3xuK2i5s8iei8OPG33WFIuNLxjjC3/NvhBPGQyxpZeYT9y7NlMkBhoQzL\nnDZVoUbaCpcu0t5ZwgKWMmEheasulvk1CahfwiwnRmp0rnvKajlGWcKD5o3M8tJokuRdp3e7XfP3\nzs5O7tzFixfp/CUySByNaP4MhnS8upa5t0uNCzFmQlOHp/bcxMg5mQXCkOu5xLk/5NBlcivvoZl6\nHgOAKF+3wC2/DqoMu+qUZ4yyAhikMqO1ptjBJpjTvWIqg/aTGrC9a8pYR1HExPPiiGbgqXbjFs1q\nylg2X2oFtSgpw/5ulAeKyRvqzoviOydvAFyVS/pctTOUzYDpD2uZAWvRcL2YLhuX6WlLR85X7pS0\nIUxuKJTBVSgUCoVCoVBcUVgIBjdFivF4B21en7etdfeIXXzJWlycgY0zbpWPrTxEvOHiU8SarS5x\nKNX97AqqJ6xwJimk6HDdVF+PRanE1ev0tj/rx+zw6LWU0MY+llaSluleynWbofG5n/LWV3GtEKrX\nKt/lGpzokFb5Vso4z0iacXepJa8ekLgkSXINiO1Gsk6sBAxJhN3nesQFmP23/K6trQEA7vvM5ykP\n6/MePngEAHD+0mmTd3k5Y3N9yDVf1HQTmWvSRm4y/+buqUP8T0yexC4yx467LIcvBHChnc6xe799\nuwll5YXojGX1yr0sR1lpvvOLLMk3o4NbRNkGSpUuYtIAhduYPrDCg5A7XZ7HhLVu4Ba5IdjrwNuL\nOdpUCAwk5z1luvM/0zFtBmXzP/um+cYtf66K9Z0lXHMZKtcssqZIy0tx9XMLur3Ory+Np1Cut3ip\n7oxb5Pe+QqFQKBQKhUJRGwvB4EZI0UpGmIyYpe1na/xxm/9msWXAwgQnxWBIfyy1Mrnm9EnSvvyn\nb///AABv+eH/CwBw68H9XNQSAKBvBdXrgIJMiISwxfqVSzJCk0x2EOf9ETdKat7mUK0xt8VmWsVK\nP4nz8teEXUa0k+KtGKd0rcd5+iPy9BC1OoW0LoyupCMG5SUpap8bWjB2pPwqqcmVsmxZL5QliGNb\nfzqPTLdUJNpyZ9Mxj7EwAW0e6tEo62GLdW4HLJV2eNwlRG/P0sldX1+nctrE6o/5XklI3rUuzRlh\neGPr3sr9Nh4k3MAJ1jSIzK13pWCqL5rkg47kEvEckUAME8N8esanhvibBdjwI8RLQzFPsQFu/oz9\nKNfzSh0aKoR0LOwmRNO4jTA0wVbuFstQrhdXkacBHdz6PIsiFCGW/kUdxuw4ck6ZXZ10Bj3egBd8\nlT5laDkhO6N1Zlwd9nqWmTyt+JB3RhbAp07Jnrr4O+SOcXY8nb2ufFOm+T/K7uUoYCQLM9BT1Lhm\nJA9lcBUKhUKhUCgUVxQWgsGdYIz19BKW4uMAgIFlcN5msnJA5CX+6A/+FACwvLYPAHDpIincXji9\nY/KcfZrYuNvueAEA4ObnEHO7wmWJHm8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Tp7g0JOYzHWX+uw4fYMlmmdjWm26mULfnN4nt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\ne4EfuYoY4lX5PvB5YeASiZg2zN5/EesDt3p5e4sdYVF5DJba1nVu8HC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jul61ZOr2+reT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vfqxUsAgAcf9IoI9HP9qZ/6KQDAT//MzwSfn7In9l0RfXvX3fcC8NHbZIPIOJAlPHPmjLvm\nNa95DQDggfvN68Qyki+88AIA4MknPQP9lFU+IFPMiGavKGHePyD68eKLLwIArl+3/l6WDV89bMaA\njCgTMgDAY48ZRYRj9jnQh45i/lKcm0kZhkPDSjCS/ZBiF8nKApJBsuN/1TxXjhd9jWXyibWVkBXV\nrEHMj0mvAX4eYw/86V2lFVVR1/SvBZr+S1qtIeeTzlfODTLq8hrvKxxGIPcHph98DrOI2H5KdUJa\nD3zii1BRxfln1c1oaK1awftsb4Xi/r1e83kM7TpfHY2D+wwGTV6Ac+1uu6Z+93f/XwDASy8ZVlb6\nUpLJ++pXzXdf/vKfi/78j67cU0992f1N9vuee+4J7kdVE2mlyClRyP7FnrdXx0CyjK5Hv2pVi72C\nKZM1k0OmLeY76eYcYyb6+lr/N/cJ7gUurbNN0ONUHHp+Xc5BpZBxcD9t4ZB/awvPXM1TaflxyR9o\nTTtpLGLcoxcLsx42t/wcv2nnFvdp7mmrYzNHfuS/+hlX9nd/5z8AAF541lgAakxs34/bNpnPhyMR\nPzIx7V9bNXNwd9tes5L+96GNueviG7sMlplzKVUl6QvNPZ3gd5pBlMonel8CzPNM/U+xV7QlSpDv\nGwl6lhgfQu4t+nvdlhQbGyQR6uCTLMul6umCwuAWFBQUFBQUFBQcKOwLBreG+W899p/8MJK+11y0\n8BcDqGvBPFifuYVizYZWl3XsTiT+hOCYHFt4bTVMoXvquPct5Wdksci0fOEzzwLwpz3p/7iwvjyv\nufce2+A7AXh29tQpc+peW/Gn+ZRu36r1u6RfLeAZ1IuXDbv4sz9rNBBvWH+sdetTeuOGP3FO5qF/\n0cCOD1UUyBJJZvLCecMe/9a7jG/vM/a+ZCnkiXHXRvCTseDzpcZsz550qVwQ9NGyGlQueO1rXwvA\n+9eOx03W48IF0zaeqvletumuu8z489lt2L7x1D21/mxyLpKd4TiNLdNDrcibN80YywhqMo+OSW2o\nKDRZRrJOWhkhpn/I+vXJ1vkoW1ZqMvVMtI4Eltqvsp+yDJ8D5yL7yrLyRM02aYUCXjOftUcR66jl\nWBmtFanTXAbKC/PQH9j3bxzUJe9H32o+u+tKP1iyOGTxqa/8gQ8YCw1ZfkLOwStXjHWC8/T69cuN\ndgN+3OR3nP/f+73fCwD4wz/8QwDx9MesX/vYx+DS0g6b+pvyfVStRvnIxliWvbBy2vrhHYRDDXGp\nB37sqNlHx2uhNYe+poFFgD6k1NFVerpurQo/y80b5pls4mZQZnvS1Azn+iJ7z3nDfYTPTFrINrfN\ns+OeyzpetfEW997zgG27n4MjGx8ym5jxee4lE/Pwxc+aPVr8zOHRh74TAPChj5rUz6fvML9l7/uA\nUfB45ZxR6lkZixgEKmvMTV8PHTeazjuL0B+5C2IMomTgu17b2AsSTGXOKqUtQJKp5N9sG8efetYv\nwcTMSE1vPjM5/yeTSVKPP9YGvbdJtK2hWHrzZdQNUtCavHKvTPna6rbE7tOmXx7T2112HykMbkFB\nQUFBQUFBwYFC+Qe3oKCgoKCgoKDgQGFfuCgQLpVh1ROfMeBJBaHZQAMWPbTmZbxooluzQV+VLTu0\nSQ/GI6bX9KatO+80LgNU0Ll2zTjbP/ecMUWMFsJJeteYJUjdP/F6Y5587GETLEUXhpxwsQ7w0WL/\ngDd1MGmAk+SwpjVpUtnetWYDK6a/YeWLdidWqH9q2nL0pHe1OHHC/m0DMWjS/PMvmiQLn/nMZwAA\n58+fd9fodrL9HHNpHqGrxtS6QvigLGPGu/vuu01dM+9ect+9JniNJl+aJDas6Y6m38XisruGbVix\niUJoJuTn0gSmg7AYeMjgmpjphs8qFciwYp9DLUw3/Eu7A8Tq0O2lED+v5ZjL+aTbpEXG+f2Jlabc\nWUoiJicTRtMuTXLavQHw5mCdxtSNZSSlqg7e0JI00tzH8eE8demtVVptaepKBVVQ+otFpdmN64rP\nhXtBQ7YPwC/8wi8EfeR4ra+PbH9Muc1N70LwK7/yvwPwAY+7u6fctV/8om/jo48+2uiHTppCyD7T\nrYTt1alIZVIIQru4pNJzyueRkiDKyYTpsjmTqZYL0qZ9tmln2++Za3eZvff8JRPQePOmMRtzbsiA\nLn7mA1uNKwHHmHPzsJi2x4+ZcWfwIAN96U5Siedw/aqZN4ftvsdg20Fl5s/I7rsrwtWsV9s5d+Vy\n8Hr8qLkvA3+PrPt9fHfLpom2Hi3rh4zLwzvf+T4AwMULPvnO+fNmXL71Dd8KAHj2OeOSQDnGXnXI\njoFPLQ27N1Kmr2/7uLPwaWqB+LPrYhrXbkRt5m4gkmZXzdtY4K5LyESJS+XiJH9POc914g3+n0DI\nNsX2RFlnrk2xPgLLmeRzgXZtwWU5ObKce0AqQDkX+KZdj9oSV8j7LBu4WhjcgoKCgoKCgoKCA4V9\nweBWAAaVT5E5FVI1azZQq6+dsMch8zkUMj+nbWDS8eMmWOquu+4Kym7ao64MONiyp3dKA520164/\n8S0AgBGaJzXtrL5Lb37bj1kgHm9OJzsT3lM5nquTnCxDWZyZDXYYWNmam1u+/dc3LENkWQHKCa1Z\nlvraTRPY9eJXvZj8+37/D8xnlsFg8Ivrlw16issuWWZnbphVn47VjxMZW35332tMgAyTQjA4RDKs\nXjh917bJsA88bR853GTH6dx/48Z2o71AeGJ2QV8I26uZ1i4n5y4pYjVLkCvTDKQMA9S61K/ZzBrp\nYC2NXFpIspa6jGTrNOuhywz6hs2QDG9qfGKBH2SIOa80gxtjE9JC53bN2j3g6FFvzeF9Jlai8L77\n7gv69/M///OurJ83QfPBpc9YkU984hPuOz5PBk6yjE7LKhlcncb5mWeeSfa5Ebxhh5aWsdi81QEe\n+n0sYUYqDWgsQKZNGik2x72cYBgcKYPKgFDSzyUbsWnGt7c37au5vwzc4zxkam8+Z86vV14xwVpM\nAQ542cqx/Z2YTraDMkOZ0n1qJb/mNhjOyhhyqjOJwGS3GQg6sL8lky1jrZjZFM1sKyUkAeCu02Zf\n/erzZm/fsr9vR46aSfmO/+IdruyLL5l5853f9SYAwEc+YublZz77IdtGSox5maz5zLSh32MqbtPn\nFRuUvAzLGJWmSzC4qWCwWH2pNuRYQP1e7kvcd5591gSOf8u3mP8DNIMrf7tc4ifx+zMajVrZTaC5\nZ+Z+L1J93ksgZwyaWdW/p3thiGPWwVTiFs3Ox67pisLgFhQUFBQUFBQUHCjsDwa3Agb9CgMrzSV9\n3Hrz0DeSDNu9994dlJWnF/roMZnCpfNG4oO+sRRuP33SpzBMCeU7qaZtf8pmal6XZrQOT4CV9fUV\nGUPRG9A30or2Oz8UyyRZP9Stbe/XNLXSNpqpOnrcnLJPrHpJGp5sztmUvX/wkY8CAJ562pzY6bu6\nvePZCC0p1bP+XfRx29mx7EHED5JtGQ/Nfd/xn/8wgKYotCzbd0xl6EspJY60JBevpT9hLBGD99u0\nY8z7KVbTlLGnxjrO8saYpLZTY44R6yLqrU/vnmEImQzZD+1rm2KTZX80Y6HrIMMqy2jfy6lLx2vm\n62wepqWU0H338nmC5bJ9J7PG5877SZ90l66UYv7Kd5g9n8uTv0tBG1pKhkzNXY2CawHgkPUR5xzb\nsQzbP/7H/xgAMB7HmCT+AdtG8/r004ZVoz870JSO29raCT7X/ZXg/kcGV6eONV1lNpG4DE8s2Uhd\nazkfnS4zlFyUfzdZ/WaCBk0uabYmxz6lWHh/P79+nN+s/V3ge7eGhAyjXleDoWV5bdrjS5dNKvFT\nYnaQ3R/ZtbmzZSxjzv++7/vRs1KAfF216cW5303sb8qli94S58bMyl5OJ6GlaTY16+S5Z3wSkLv+\nkrFQfs93fxcA4Gf/9n8HALjX5BzBZO7b9MCDb7R/mfrf+l1vAAC87z+aREDPPf180E/Ax6wsarv2\nZ4YVryv/+6OxjE+mTkiSYvgkC9jVpzTnF6w/l/Uz7oRsPhML6b1a+t3GWMbhcJj1P06luM0l32nr\nh/y8zcoY+zxlmcld0yY/FrPS6j7qPSGUbuwuayZRGNyCgoKCgoKCgoIDhX3B4Pb7fRw7esQxSfSZ\nBYChVS+gnyWZyFfPvQzA/0fPkxYA3HOPuf70CcPQaqUCMjNO6BvAtvVx0pHgLo3fpmcMGydNy84u\n7EmZiQ2CgxD9WqxDHJmXyTQUlR+M/amYJ0rHZtk2feZznwcQpqC9YNP3kglzzIg9bJGd64mI3U3r\npza3fZvshswLmbLDQrz81Elz0qfg/F94o0mTe+qUieqVigtkVs9fNEwInyETSMR89fTpkGwH/RN9\nek1/IuSzZ/s1QxkwSdYntV7EI0VjjGsqTXSF0E8qPDmbV+1fljv96vqdLyWaKge63fq9S5ix5hmG\nlG8VWaLFwjPp/E6n3+UzddYL6ZNZh2W1QDifj/R9pxqAngtMNsJXwKel5frVzK3uJ9Bkrb2/3a4q\n68eW6azX1w0L+HM/93P2fmQc/L1o3NBa9VeumLn+7ne/O2izuTeZbPp7mzHU6UGlZYPzSFsy9HOS\n9VfKAkBWNsbqLBasJ70OTLm0+LpOnZsTnNcsTWx9zCahgk49D5k8joVUlDh6LPTr577hUqqK1Kra\nZ/LcuReDtjhfbGFV0+mi9V5Ti1gQzvPdnVDJgfXy2tWFZ5Wp4HDnaZuwwvaRe3FNFYU3ej/kGzeM\n1e6FqVkf//Sf/iMAwK//6v9ixqkvGOhNs86YJ6ey/sHjkanv0OFB0GYAuGoTkexsb9iy9ve0DhOI\ndGFL98L+pRIBSKR8xmNsaeo+cm7zWb35zSYxxppNHMI9yN/X92dry6zXwSC0tKV8TGNoUxaIfdZl\njNvuE4NmWnNl2r7v0mf9nGN+uzmFlhgKg1tQUFBQUFBQUHCgsC8YXNQ16tkUFyzTt33Tn5LuvsNo\n+pH1I7vLExaZEckK0c+V2n6XLxumk8wGT9Dy5D+zLAqZEZaJafM6fddpmJKUfrS8VmR49CyW9U9b\nXTWsI1kI+g3KKN8rVjeRp0bXx0WT7aCWLNkI5182Wg3avHXV19/rhVq8ZMs4xg/bKO8TJ064a1y6\n2pFp96aNUn71z42AJyOPAT++ZFjpF6d1XamTCwCrh9aCcaHqBBk+1iFPhGRn+r2QRuN9dnab7Dvh\n/E/Jwi84ps1TsPOrrbhsQhZqLnzd+oM8GxFLO6n1AYfKl1j2r+3U69h/4SPbYDMR1yOUffFznawj\ngvrlc3Bzrx+WIWhF4FqW13Md8/lyDUlfQLbP+R2rz2NMT9rfrhfUtbPj2VJG6ZO5PX48jNqvxdTg\neChXX/zar/0aAG+1WAiLAbVYPYuJYAxibWefyC4fPnwkKCPXHfcwMrjsq58zTWbG3yv0e9Tf5/Qy\ntQ5urP1ELFJaX9NX10wmYfpr54st9uYb1wxjy/l6yCoX3Lhp9lKZdpz+k7rdbq+xvtgLwY6vjAdB\n/c4SZFm7yY7/HRpRyWRuLWXOJ3duv7dWNZG2e8v69C5sGlztf7ozNX1dO+QtM7vbZk8fD01710aG\nBf7rP/Y/AADuu/fbXNk7Tps9/c57jBrAcy98AQDw2c8Y7fPJxOylGxsvu2tuXDdW04Hdf7amVt0C\nYXxMLOq9i89kTjFAIsdm7kU/Vl9DKyQAvOENxjf59GljsaQ1RVua5HtaYObih1/O+5hOrbYy5tq9\nl3S1bf7rMdzKWHbxk0/5WufamFNoyaEwuAUFBQUFBQUFBQcK+4LBHY9HeOi1D2A4NNmrVoY+mlv7\n+pHNJGtH1mh74k/O83MmctlljlF6ii4S9pLPhuUY1ll4P75ubvsIcXdSVeoJ6IVMQE777dJVc0Im\nYysZaMJFzqq2DJxObcQn076SDRpOw7ZIhYojRwyTRj1OZhYbOx+i0HcZAC5bX18+h+1d8xzutSG7\nJ0+dcWU5zps2SpxjwGvJ7EpfQ35HdsZlrSJzbFmXgcwQQ/+3WchM6WxlMZDdamZKaTKsjiGswmVD\ntjNQLKhCJr3XC9nS2Cnes8lkFe08tc9DKlSQfdespc6QN5lFIv4X4TzlK/0vAT+ndcY632ZqzzZP\n5o37OeY1VOkAPOvBecl1wH7IKGU3F/i8Vf05drzBKFkWc9v6Gq4IP/Of+um/CQA4Zplb5a4N4Xbn\n2sA58O53/w4A4PqNq7bsQJVs+s2ORmEWN1e3oIpZlgwknwvfc6+TffJ+5RwD5b9WNRk3PbdzTFwq\nujqWdUh/lmJxJKbWZ1tbMrRvdyyDFP2at+349G3947FfQ1z7BJfvdGrjI+xvimSI2UyW8f2ze7VQ\ntRjxd2dG3/Mw8xfVJnJjq31+ZzVVeERmTWulGfXN/Y6tGwvc2WeMhvgXP+fjIqZTcz2tdJPalKmG\nZt+9ftOq7Wx5Sx/16Wd235ha32iq7nTxE81hNg9Z/C463Q3LTIKYjO0Fei/j50888YT7jL9nXGcp\nHXCtEavr7/V6WWYyNU6xelNrZy8KJF3Y0pSf7l70dmPWoi5Wolw9ne67VOmCgoKCgoKCgoKCfY7y\nD25BQUFBQUFBQcGBwr5wUagXNeaTXexsWgd3QZtPrX2QwRU0GWhTdoxqZyDG1NL9QytvMhqHQR7y\nb1pLbM4AzKzZcHTIyxXRRMZ70x2AZl26HWyLgAOWuXbNmIS0c7lOFgHI9Ld1cM3UOiJs7XpT73Ri\n+kiTL83/OsDrxAnvSM9gGrbh+pWrQRuuXjZtlSlEe3acRzagoe6bNv7RH/1R0GbAB9Nc3zDjoZMS\naPMb4M20vJbgWMdSArs5sAiDkCjcHjdrxM08XhbLLw33rJT5lokl+lXTJDtf7ARtY5tcimMx9xoJ\nC2w9K0oiKOaioE1QDTOPdM+o41I6Om20bB/dFrxZqSni7/o8jyd6oHyYlpECgMn1eJKD2dy839pu\nyjrdvHk9aG8upace0551PdnaMOvx+EkTQPljP/Zj7pqTJ0/aa2wbrUnWBzhI1wEzPn/2ZybI8vp1\n4yrw+OOPAwAuXzp4t74AACAASURBVDJr6vnnn3fXnDlzZzAeV696V6kUtIsR1yjXiQy+0AGAWmJH\nJzkxZe1zrcL0qzlXgpSLQs682vVz02DzMpuG489+XL5sxk0+71QwihsLYQ6f2FS5lHVsBnsy0NLv\n43rs/O+GnV/Ca4zpxtm+Vet6t2hIHjVN79wnvGuHdccYMbDZt+PwqtnHJ9umzKsvG5eEY0dNgNTm\n7ldc2aENDj536WkAwMwlb7DB2jbI7PAhsf/a8ZnOzFpdsQmZdnbS0k/LICch1Ya2JAS5+UpwzD/8\n4Q83yjIl8mOPPRa8j9XpgyubdcuyscCzlFk+1n6dAEOb/LOuHAmXoJz7RJekE6n3RCyQsC2wLtaP\nZedKYXALCgoKCgoKCgoOFPYFg7u5uYFPfvKTLvhkJljMnpW50sEnDMAhCyn/s9+xZbXguJNOsgcD\nmebSCWnbkwKZUJapxyK96DbTlRomiQkXdiehtNFQHOfJAgx5incMmemrO83riBZR1smM2GOJDIxZ\nWzMfHlo7HPRVS3NJoWomzZhPVGDdLDwtrYggjrljncy19dDch0xxLcZ0Mmeq4TC1qpdqMuVibKxO\nLOBOrWgKPm/alJe9uRq7qnna8yfY5r3l90MR6Mg2kcF1LFEvDP6icD8AjOx8cUFxg1BeaDJpWg/0\nK1l/fWIHPEvm5anCfri6IILAEsFlsXMuA5w0y66tCiGDETK4jqlfmM85FjFWg9+xz5r5BvycGA/D\nOaLTOstr+NmwHwbwjcbm2p/8if8WAPDw6x5qjIGT26p4f1pZ/Jg+8+yzAIB3vetdADzT89JLRnx/\nwWUtnt0rr5yzbePcYGKPRhMcdGAa2Uuyy3I9sAyDmFLBInyGgEwRG79/lwAZLeOWY5J0vbHvXWDV\nLOyHC6j0lbhrOA58dpwjnIMxy8lQBUstHGsdlpP1c746uTC3p8mEIbtBWfeMXLKRZupq3WfCrffa\nWMOGlQ8Wnk+s5QJmHRyxCUomMxt42PMBYxPL1NJCiXrVvjcWyrWxTZoj2NmqMu0fjcxnuzvWold5\nq2YbckzfYNg9sJFoBC26trYHUaXuE1tDL7541tZjyhw9aiUDrcIoLR+mjHmV+8N8Pk+mpI31MZe0\nQfelkRAo8juRGstlEkno9ZdjlVPjHbPq6Dq6YNkAt8LgFhQUFBQUFBQUHCjsCwZ3dzrF8y+/LFgp\nwW7BnB5T8hA3dpo+gd6f0jDC2m8zJkHkEyWYe5+/aJhOMgHz2XOurGdIQnbLMQtMYCBOLezT9rZ5\nZUpG+jFNLevLhAOyvvGKOWWTse0PTLslW6DloRwLeDP0D5YnKe2nlvLl2dyJSKFY/1ZYOaydrevB\nNfJvfjRzBEZ4YpPjRD/RnWl4apwtQrmqgaiDDMzYplNs+mY25Yq0v6b2H4xJHPmyccmvqvLP47Bl\nTt/2PX8JAHDpimHc/uAPjZ/XQgzBup0LnIP0EdesqVwXoxUliWY/12XlNT5pg33OQ1o2QmYaAKZM\nOz0P++yluSxzIjSzhgjZJ7btiPUTreijJlhlggzJcWuxcckiqua6H+q8uGyDbeNCWk5qphW1z8hO\ntbe+7YcBAEfvNMztF62kEgBQPWpgq6XfJVngnS2fLOBXf/WdAPw+8bEPfyLoO5sSsO9z+lFyTZIp\nDtfFlasX3N9cq7RyjVdMxVs2zXa98Ow5ZQQXCOXBeo4tZWrxiEyYGu4uou+NMpQzFPuLjC0A/N7r\nUg4rn2J5L6qbzWwfB9b/nJOeVrCwH/a5K1o85guYYqjcXhrpq74mlkKU87ThN0grEccnsmcyyQVH\nduD2K5+ciFjA+qkzbsSZHFjC/8717d+1tnbBSjlWVhpN/DS439O5rbB3rNFuIM/s6bokUr6Yy7Cx\nlIKkpJmrSvrDLkJrgYuhcD7r/hky4U9l95/Ll8z/Ax/8/Y+YAv+NefnDP/i4u8bHSvjB+9NPP9mw\nmMV/I+0rmjEmro9qHHzsRlwi0nyn40Z0Xfa+vfT99JoJfxsHYVvUe/dsxfpI+f928QteFoXBLSgo\nKCgoKCgoOFDYFwxuXdfY3Z06Hz55Apnb4zsZAM1QxdhYL5hv6iHzqX1tZNpG+s3Sb8qLpFsWdeTb\npP3hlvFvYTu1ODnbJBkM+s/yM+9v1EzAQCYk5RPG+8lED/pE6VOHpllA3bfJVPtdNiOa217pvwug\nkT42lYI2Fjmt/VF1BHKs/brPMYYndbJsqh8I9t3W/0d/bE74nCsu1bBglcn0rFgGms9XKwCEjQif\nDaPD6XcZizblfdzz9Z5rzbJ16M/l/MocY9WMauVfbm5MwnSjsIL0cl14a4Gt38mY2NdF89k5Fn9k\n+jOb2rFwvrKe4d2wa3zN+qY//PDDAIDPf/7zAIAvftGoH0h/9u0tm/rZPqPVMVOumvs8+eRnXNkb\nVhVFW3V0Olw5Tnp9pfzTvvzlL7u/dTyB9uuUdTQY/6q5D2m0sSVdxOQJ7kExNQv2gzEO2pdVPgc9\nPtoSF5vjes+KJZ0gmqoJ4b7BV8k+d/EP1fXra5dBzidzr3V0qSfLMkb8jVN13kp7l0lkQLWY3Hhp\nBlevzdCiGbZ77qwuYTIWmV7bf+fn5ZUrV6LMbRJV6E+7jB97bKgXkZieWNnwOYX7qUeTaXd/00IW\nsWaaK9MBBl1UFPaKwuAWFBQUFBQUFBQcKOwLBnc2m+HSlcteL3DVs5ublk2hfwvZzJXx4eBzeQLh\niZ8nb7KzZKE0yymh/XT5/uamj0TV1zVYAvt1FUn3Ss1FsmejFcvorhrmQjK7vM9Nqw/sVSbCUx4Q\nZzZlX2V6VEIzR6mTc+4UqbMFL3WKt/6RmxvNti2jb8hzmma5fJk0A+0YROv7NOiHahrRsnU7M3Nq\nNUxLS/3JkX3OM8E+Me3wypphsqfaF53phMVtHLNqtTbnU83cWhZ1FjLsgJyvIVMV6DMqbVz6pvcH\nesvw17iUqoO4fzOfd9xXz0bgU1O411Rp0P6PdJlbPcQ9wNx3e8credx5p4n0fvDB1wIA7nvNawAA\nc5vydD5rrg/2Y8vuPZNts38887TREz179gVXdjSIawuTkY7NDc26pvw4qacty3JfOnXqVPSaGFI+\nbrG4hpSuchekmD0JnS6drBfZfsmQOQUE1UeOgbZOxRDzqW+DXu+5cdLvc/tfl7FsK5P7zerSx1Q7\nu0TVtzGrXZjiZVQNlmJwES8b3K+Oqxlo9Q/Ap1ZPaUkTUiN5MqFFIPxfJNf+ZZ6DLpN6Lx2P+70R\noogoDHk0f2/kfeT/Nbz1wrG79r3zZ7bWyIG8Jv6Mlvm/oysKg1tQUFBQUFBQUHCgsC8Y3F6vj8OH\nD7sTOf2zAODEyZWgrD5J7ezaDCw7nqkiO0BWQPsexpgG6bcHpJUSgHQGEe0PLP3Jhlb5wPnvTunb\nGGoh8nOgmSHN39+8huxWnqnowqx6H7R23yqi12+yySnUi/hpVT6HlJIDfT5zpzvtH+XLSE3HeKQ0\nGUPOHZm1rKm8EPczkrh23bBvnMszy9xv3bAakmKM+1MzTze2qaQxCK6Jjq3zubUn5Tqcp57dWmlc\nmvKFlmf6nmKpGRk8skz0fBHOX0A8T1oweuHJfDhW6gdoPk9G9i9UBjX9NwDsWlmOgb1mZufIbOZZ\nlpOnzwAA1mxWw4sXjZrFvDb9WVtlVkN/zZa9fmXVsB9/9rnPAgAuXHzVlvUjtblt128/HC/GE3Rh\ncPX+FIP2FXdsU0Y810UwR1QrUqAPvF9//hsgzqhrFs0R9mK9O99YyxzVi5CFdxrTPvQ/otcc3jf1\nXiLHJqf2mi516L532f9uR1R4N5WXvbOm+vMclskqlWOZl2FsU/WSVGxcW3fn8GR/tHWrTvqyyv6w\nzPJqAK7dWd/Y7vUR84Zahrtj/P7BjZQFIzKWHKd+xD9XYhaxJKawDHvdhsLgFhQUFBQUFBQUHCiU\nf3ALCgoKCgoKCgoOFPaHi0JVYTRccSaCya4Pftq0CQS0uSonvUEKnwFoNF1rU468dnvbyAnR5Kol\nZAZ9fxbQ6WS1zIx7HzG3EakAuDBYznzGlJ68bywhRtu4xAKJdF/3IrRMh/pY/U03gNbqHFwigSVM\naNpkpgN+JFyfXTBTem7QmtOUZEp3aNWmn6QcmJPMAqXRvBvO1Jrj3Ty119IsFjOD9tQc76vx52OY\n1ukAu4qF+JzQdMPRaSC1C0QQ6hdJrGGuDdsUM3M7zMNnGHq1mAo4lmMbuEBXnoldL0ePHndXzOwY\nPvPs88G1NcIAprVV78rB57u7Y+p9+cWvAgCGI2u+F8GZTsyfaz9iytf9cIlCELrW6Pkr9wy9rvVc\nlGUb8n5VfO3IOe7WA+LQzz/Wx2Wks3QwLyHdAWbz/JrPuSvdisSQ7lfMdeR2SH7tpS16PXa5pktb\nlnl2Xe6zjOtGm0xUlyC6Gon7LTHkMuhau+nViw5tUC4v8rO2a8QnwX1uJegPAEbDw43PDHLuJfHf\nUb85pIMu/atqfyXdP27dVacrCoNbUFBQUFBQUFBwoLAvGNwaRj5rah2R57s77juymKmTkA6+0H8D\nnhVwgvA8BQuGVbO+DBBjwNjuzAcwaZka3ZbJjALlXgRaS3KNmY51OGIDwlf4wDRKV+2QBZyFaWvl\n32xTKoAiSPc6CiVEUif0/Kk1ZJ5jbYqxu7aE6V9DekrW384EpKTdtASVrM+9Ip7sIgYGwnCMOX5k\n2OV8WBmGTLB7PpadRdVk96cLzfKng4MGbrqEzFuDvROpn2NrJQVtEdCBk+wXLRCmvUwFHMrx+SDM\n5n1YD2XIdEIXKclGFqVn+7S7a9ZB344/01+vrnnWYmvTWkjmIfPFYfFMqB+TjQ0jD3buJcPcHj+y\nbuqwe4CUpSJ7XE04T7sziG5cEsFBseekn0suGUsK2XXd0uxYcFOb7A/Qvi/5eeD3ySoh3q/ryAXi\ndJEJS+0xLvhyibG9VdxKYoQu7K7ej/Tnt6tty7C9eh61SZnFbxi+9Yxr+vq2+4X1c76G4xabG219\nj1pOVLBcDMuw4kT7Xt/8vWtaDfT6bl7j+uSsjmEdvWF3LnUZWbU2FAa3oKCgoKCgoKDgQGFfMLiL\nxQIbGz5trjzFkzHSvmaauYixBS6lp6WOUv61gGfj+OpZWpvu99DRRvvoO0cWy6d7HTbqJ+vDAx/b\n5tL9LtKsR68KU1aOIlRY6hTv0xYPgvvKskRKPirnM5liTWX7Y+yu/DwnBbWMT52WdPP3a56YRSuC\nd5qhBMQcU89IM5Syf7NpfPy3J+YaKWk2V35XPtVpenk2RLjn4RxZ1HYNDZpMsU6EolNPy7Ka9eU8\n5v1jjHdqLpKRk+NUu3mjpens3BFjwLW4uWlY0/GqSYxxz733AwBOnz5t+rFoPrtGamzLGNMnd8sm\nUwGA8+fPA/B7wc0tszfRcjKQfqi9sK+LRTztbmyO+wQPU9u/cAykBJtmuVJWC/m3H/d2ts6vxWjz\nl0IsDiAV/6D3hoARU/Jmel41UhILpPacWH23w29X1xlDri1fq3t2vTbFwne59lb8bGPX5/xPk/Uu\n2udvStotNlec66gqk5K6lGW0NbkLE60T23SRYOvCkrePYdrHt3E/7Yor0GOATUKVb17H98Voi26j\nlaQwuAUFBQUFBQUFBQcKrQxuVVX3AfjXAO6A+Xf/1+u6/uWqqk4A+HcAHgDwAoAfq+v6qr3mFwD8\nLQBzAH+/ruv35+5R1zVmi4WPCBcnEabQ7Mo2Ak1/VC2oTjaH7yVSot/0y9P3Apq+rGTeYioH/X7I\nJs9ndbQOeT2vZTR6zNeqzV82Jpqe8sPKneZ1/Xw+MT/bZoKEuC9ulyjovago+LZmTsPKnyjns+c+\nUykfY21iEK5jZSehr3IlmFXNpHJ+ekav6aOpGdxewnlrPshE4SbYfsDPR/fMbDWaaZN9HgyV4Lz9\nyj3/QcjoA57B9Wdtey3Ca0y95u8V62P7Hd/xFwEAd9x5JwBgMrHPTiiecI27dvI7u+5uXL8GAPjQ\nhz7krrnjDsMELyyzWtWhT/FcJl6pQiZ4NsuvP0Cs334oIq/3o9ie5v2c2bbuLFeOsXRWrV5zT+xa\nr7ZkxFgovafpvUBes6jjZfReJ5lvzcZpq10XNFi7DOOtcbvYp9tRT5cYii73T41Drv69KGzspaxr\nW8v3IcL9NOZLTr9TXabSG2/kXlKAwfytfiei7KzyWe1geeiCqoqX7VaHssDx56+OWe3y9daJdsj6\nU+9z9bahC4M7A/DzdV0/DuAtAP5uVVWPA/iHAD5Y1/UjAD5o38N+9+MAvgXA2wH8H1Uu5VNBQUFB\nQUFBQUHBbUQrg1vX9SsAXrF/36yq6ksA7gHwVwH8ZVvs/wLwIQD/s/3839Z1vQvg+aqqngHwnQD+\nOHkPmJRym5vbvKdvoP0XXKch1GygPMXrsmTGnKatZaokY6J9XzRDpX34YqgUGxU7dZBlcu0dxSOo\n5Wd8HVJxYZFOA5piQGNsio6wvB2RtDHfVc2i5PSIU+km9fPI+hx28CHWfqGE8zGNnKBTPn8x/2ky\nelD+tfS3rafN1IWawfO+uNaXXKQPhhrDQS+c25xfu0inSOwhPU4a9UzpNMciaefxteN8fCehHjXg\nlUGoKsL1QZUJWjwAz9ze/4BhbM+9/LJ9NSl01w4bn1zpu8rnyTZwfC5duAgAeNnWcf36VXfN0PlN\nhwwrUzT3x75NnnUfBtekdIQBoxgjv9NsIyHnE79btamFXX8SSgbynrV6n2NIvLWoux9kKno/tydr\n5ZTY58PRalDGxSsk2irvSctSF7Sxsdl1sQdmMucf3KaE0OW5dLF2pVjxLvV1+X4vEf+3gi6Muv87\nzuvFWGsi5RMbWKOc4268zra2yvK53zkd25JSJjGfhb6vKetErp1edYqf5+ZDwn/3FufBMvuRxFI+\nuFVVPQDg2wF8EsAd9p9fAHgVxoUBMP/8vigue8l+puv621VVfbqqqk8vFm1SFgUFBQUFBQUFBQXd\n0Pkf3KqqDgP49wB+rq7rG/K72vxbvdS/1nVd/3pd12+q6/pNOmtIQUFBQUFBQUFBwV7RSSasqqoh\nzD+3v1nX9bvtx+erqrqrrutXqqq6C8AF+/k5APeJy++1n7U3JiJ5QxeFNteEWHBTiob3ASGevk+Z\nBpybQ0TQOR20kQ6ycPJH2nkdzX4kkZAiulWkTJhRM4xCTrIndR9CXpMyZaQDyDxSYydNKqmgAW2O\n0ZJN8ruUCTBooxYEr8KyMaQklEQtsnT03lqiqRpGXF9UE3LmIycH5kT3KZlm7zsLJXGApqQf2zIc\nj4LPZT/onsR10B8yyMwHX959970AgG0ry7dqZcK2tkwA6HRu7ru+vu6uuXHjWtBHrv3nn3/W1GVN\n2fPZxF3D4LKdLboC0UQXC66IJwrJmw3ts7L1jvvNfQ8IXVYakmuRAKtm2+wzS6UxjUDPQe1GIYNh\neW+9Nvn8c2l9NWJ7T8plQ+/vXfaPZU3FbdfuBXpsc+nmiS4BOKn73K5gnb2ah/cfvgaiURFXrdtS\nbSx4ni5q3JO5B6tX+Zz6I3ONn2vcy8L9Y4Dmb7zbc9RvZFXH5hX3p3g/vjaj1I7WJ16ZFv5LAF+q\n6/qfia/eA+An7d8/CeD/EZ//eFVV46qqHgTwCIA/uX1NLigoKCgoKCgoKEijC4P73QB+AsAXqqr6\nrP3sHwH4XwG8q6qqvwXgLIAfA4C6rr9YVdW7APw5jALD361JcyTQ6/Vw6NAhx1gEbGzi0KhPGdKZ\nWgcjaKYhxrilgphyDIk+IfsTejOIoMkM63ptPyISHJoNJIPbRZ6li5RLg9XsEnTkWOsmi6LL5BgF\n+b2BCuBSdeQRZ+xlqkGmGWQihGoR9tVLoYiTs0sYwmcWH58goGGRGEPeX36UeA7MCqJTBMsy2iLA\nMn0rKbc1bQ+2icrOkRWo4sywuyayPnVyC2Jmfe17Iliu6pt7rq2t8AMAwB1n7gYAPP7Et7my7t41\nr1kDIIMYm+t6dcXc6/r16wCAP/vC58w1Nh14bJ3Xc8Pmku2lspgLFJx7ZpHBd5rJ4Tv/TJvPrs/+\nzNpjEHTAJl/7HRg919YugT+VYmKqcF9aCMH2WjH2OlBM3sbFJrYETwXfz7Wlp7b1c+7zefsyDARk\nGfmsInfNfOeh04422tl6fX7cc0GwqbqWqX+vZW8Hvt73a0JaEczr/x+YaNdG8du/qOK/jRxb/v8k\nx3pWWXlShP8D8dUFAIt63RxkcG0/vJ8cPcfc6iF1qY1tuVQGiK8xuqgofAzpneAHEtf8IoBfvIV2\nFRQUFBQUFBQUFOwJ+yJVb6/qYW28EmX6tJQVodmuuhYMTMKftu6Hdcn76BONrmMekXVqnE71KVUy\nr2QiLVNUL/R9mn5OKTaWLEWqL/J97uSs2Wl9Tc6Hy/u/hUe3GBvRRMiW51gJjVybOC45nzHnj1uF\nrKVmcHPjliojWeaB9VX0cyMv/xMDJa5iLCNZUD8nQpaXbTlUrblrtO+f9pmtxVpzKX9VamnN5M6l\n/yOHNiGDNOiPGp/Xlh5ggoHXPvwwAOCB17wWAHDDpuUFgNUV43N72MqBsQ1k665dM/62h9d9n6dT\nI0325JN/au5jd7zhwEhQVRG/tZllVEdDSpXZ/tgiuwvvr9tfMe3WzEiPEmnON7rp78+yo358G5bP\nO+WDfrvTv2qfW7aBz12y8jpZgy6bYya77E8pSSM/N9OJJJg+PWeB6wom2Mm1kegit6Q/z+1p+nWZ\nBACxffAbxV5+/Znc7s8s3iaO0zeKeW4iFQOSkz/FMPyOoHWKMQjT3Qk0mIyIe3wv40mrjRzuebvK\nkpd+TVFS9RYUFBQUFBQUFBwo7AsGt6rMyVSzIAAwncYjTf379vg8zQLH0u6xPvq26dO2jB5OnZSz\np9M6ZBLa0k/GviOkH1zmhuq1CV1PvVj+dD/or4TvxUnRM1UhW619WGO+yu4E2GtnJfzF9EEK2YIw\nmjv0K/Kfo1FWtyn1ngyPvHY41Glpc9HuKUab7807OY/nsH/XtHpYn8z+0L7aRA+TzUa7UyyOnPO6\nTGq+5hirpl8W2QS/lhb2jP3mN78FAPCaBwxzu2aTOlT9ZjKWU6dO2e/Mtbs7W/YaMxel3+W/fOe/\nAADcfdcZACIy3yalmEx37DV+bOv5MKhHz4jRuMliNv2+1TNd+DU8U897MdXrI+wvkLa2dFEtidWX\n+k6rQnBucj6fPn3SXaNVb9hGJqOIQavgpPz/AeFnrFKua8UWOTdZ5vLlywCAl156KdmWrmzmMqRj\nrs7UM8xdvwzTnfpcfn8rDO7tUFO4FTWLPDgH9r9/bRe4uVI3fxtTsUQxa/fQJqDpW1Z3ODRWwdHQ\nKp1YS+BOb8ddw3XnUwpb613k2fUb1kz+9g6CNn6jUBjcgoKCgoKCgoKCA4V9weDWMGwiDwySlaA/\niWZ3/SmmmapSs7v65BxL78sTyEj5TrKsT72KxjW6vtjJ2Udgx/3WcsyhZiz6/Wba4NRJKXeCSvk3\ndzl1uf7MyKLAvsox5bMJU9ASUX+yhK5gG4sKAIM+maMcq2XnDcK+k6mKpQ92TUv6REfSRaONoff1\nu3nvItWtNUH5RckTtFsH6hF6hsyeoEWqXs20cW3FVA90X/U8jT67yFqUYDre/sjPh7f/4DsAAG98\n47cDAF559YK9n2VRhXMX/cWonrC9s2nfG+aWeri//a5/567Z3TJlZlYZgc127MfMsrSivz1q8CKu\nEjAS47RQ4+CihjUpFWFitE+pxuHDhxvXEGRJ68Qalu3VFpMYtI+n9s/mXDl9+nSjfSk/1C6azzmG\nknsu753S2pbX8hr64Or01zkrRZo9bVpfltkjiVR8R+yaFNMd0xjWbYox26k2fTOj21jsxRf31llL\n58cuPmvGHcX3EbmfbLpU4qv21ex3XBej1bXgewDY3TVxCw3/XNsYuYZmjINoyF3F46C+3igMbkFB\nQUFBQUFBwYHCvmBwe1WF8Xjs2Ch5st3dpcZlyD4to0+r/cpivpn+pBz6MJLVWj+Uvk+Kjc3506YY\nDNl3He3O7yaztLbjMj4vmsFNsSm50xcj4/NjmvCzq0Jtz1gbUn5GdUQv2EqbNnxW5YmTfpX0P/aW\nAPPKU2tsHFOsSowBHfTCpZVTwPCKBKH/98p4Lfi+Clhfmy2vZ/tWh23j63DkT+Z6bH3ZdNY23Xdt\nQZnPmvM1lXWL5+nXPfiQu+by5SsAgPe///fsGJjP6Xt/6swZV5bWlStXzDVrhwxzu71tWNqPfeRD\nbL27hmXWVo4CALZs2b59PnPrHy73k4G1PGh/fPoUy7mhy/g1a1VZ7KYWGycipv8t+5sDmcoubKlG\nl71C720rK97nXjOrfNVWN/l3Q0dZrfOYJY6sbCrLoLyGa/DIkSMAvBJJF+3zNNr98l3JSPY2rVOq\n2fHU9bIsXzknYs9Uq0toa6Fsd5vf7jfad/Lria9tn2vsRULArQ9xbZsSSaz9U7sPLeyPIy2uo5GZ\nG4cOmX9sxiv+d2I0Ggevm5tmz5wrDW7A/2YxK6Mjcl0AxjfWYlAY3IKCgoKCgoKCggOF8g9uQUFB\nQUFBQUHBgcK+cFEYDAY4ffq0N3EKC954HPoGaOmpmLRYKu1uU66qWW/KjWFmBePjbWm2Qd8nFdDQ\nbEsHs+Gg+dg6SZUp0JyXciXoJCKvAotiQRw6wM59j6YJLe2iMFevTReFXi80jcYCxlwfK+1GEvZV\n1p9ysUiJscf6nhW4Z2DdPDS90vxTWYkxGQiFnmoTUkFgQv4q2aawnxLaTKwhTe8sSxMyzcM0q77l\nLd8DAFg95Nf0YGCe2as2uOzhRx61dZjPx0JyivWeOHECAHDh4qsAgA9+8OMAgI2Nm7Yd/tkdWjUm\n9enMtP/oUeOq0EhyIZ4dzWyjkU2eodwzGIQBAGvD0I3Ev9oxsUMqZcJSe402vZ8R7hn62bD91zvJ\nR8XN0rF9H1rUsQAAIABJREFUkH3k3qBdE+Ra5ZzgK9tEN59A1k65r6TWjmyTTr7DOrTbgbyG3xGP\nP/54UCb2O5Hbtw2aJn6NLi5aW1tGzk6PW2wv43cbGyZwkmZiPodYEhC9zy7zm7DM78Y3oxvDrWF5\nc71z8xE/mam1knvOfSYEsveme6NP+U1XBX8jriEm31lfXzfX2jm5u+3Tv0+ouujWOl0V7G+vCub+\neqMwuAUFBQUFBQUFBQcK+4LB7ff7OHz4cPQke/KkERZnGs7r168DyEuuaKaQjItmLCU8i6JZOhvw\ns+aZpLagsniCgTiDl2NJU2XjwVN5wXkdrKD/lujCqniELEggjeaYLy3JlZZHaiS1SMjDybbrelLy\navLvlCRQ/HlwLOPX8HOmeDUf7gT15QIPm/OJIt/2fSSwUrMoyeDFXjNBiRuDBcsMZDdZ2NyTbVPy\nWp5NS7eJ7Nlb3/pWU4eVB5NBCmQqaphneNddxwEAo6FhqshcAcCRI4b5vXHjIgDg4x/5AwDAzsYN\nAMCgz+chn2HPfmdYiZ4LIjWfj/rNAK8mWxruNSurnoHW80UHtMbQmIOL+Nw7ZgOlJDjXGVxGdkXK\nYXF8m0FGodRUzErBINvZlEGEnCuGvfnjj3/KXeP2HISMYWwfb2P7YnvN7UhD3IWZ7CJBqOvrIo3W\nJuuUC/jSbeDz3RLsWVsbiUXL2Kfa39ammHzaXsCATI/ln3uVkMLLBQtTxpL7916krNrndfAuWS71\nuydJ33Tz6mTtIyZzUrqFHPHpjvnf6OZ0w303t0l2VtcOB9dQmlBa1ba2zD60sWGsE/xfa+Hmtvn9\nGfe9BTApx2efQyyRVe73M4fC4BYUFBQUFBQUFBwo7AsGt0baX5XMrWZh6dcXkwvTJ2MyFl0Y3JQP\nqGQM2/wqcwzuYBCXj8qdoPVn7HOMWdVlcwyu9qtMMQxd5H5i7U7J4egxDVhfxQJpFjbG4Gqmqs03\nWt4nJcYeK5u6NjafFpbNTSUZifnqaSZX+0HKuZOyHui6BiKpAseJAv30CSQ7JH1LKR/DOUK2V89f\n+ggCPoXuE088AQB4y1tM+l36DV66auS96AcL+PX96KPG95bP6vIVw9KSyQW89eb9738/AODs2bOm\n79ZplgxDTp7PMc/DcA3F5iCRGmN5PUE2qAsLyDKDYTwRSo590glppHyXtpCkfDKDxCTKOpQSkZdj\n49a+8qWP7QldBd/JgAPAZBKuxZSlLPe8Y+3WZZZBF6ZTl9XWxpQUWK6Ms5B1sPjp910YYqKLRZGY\nz2+PD+7t8OXVspF78Sn+RuBrfW+/1+S/l3OQVrMd+3vAtOmsYyDkC5l0x/+GGOsRLUysI7ZvaKuv\nW9/WclmJ5BF7HafC4BYUFBQUFBQUFBwo7AsGF3WNxWIRPR1rIXMtqq9ZA1lWMxWpKHiJ1Ekhd7Jd\nJqoxFY2eO2VrRiTW5zZmNeaP2hYNm1MHILooLugUxpot4jPOtanBTMp0qQnR+FSbJfSz0qk9Y2hN\nYAGgX8XbG0vNrJOX6LGMpQJO1dfwCRXTigwtGdzm6dszuGQEGRHPMi69r03wQR95APj+7/9+AMCb\n3vQm2zbz+SuvGIWE+x+8397f+5aurJBpM+8nE/OspJ8u8cu/9M8B+Od91913BG3iWDCZRzAObh3Y\n7xLrRX6mEWNwm6nDQ9/9HNr8Bf/1Q+9srcPhie5FCwoKbg9efeXs0td8Ixlj9z+KSvIT2//8/ypU\n/zB7p1QqGa2Y3xD+XtCy1Pj/TOznKebW7av2pRIxLzX9pBspgfMoDG5BQUFBQUFBQcGBQrUftOyO\nHTte/6Xv+4Gsn1QbOxrXRW2yfalr2iJbuzC4us0xP8tloNlX/RpTUUj5rMb84lInyZyPW+q7WBpN\n3WfNOuZ8DIk2lYAYlmFwNWLzSCN18pRtqueLaJkY09o2prpcrg0NiNTAOlUoQbZU+nG6y5ny117L\nFKh/5+/8HQDAKcHgcpm98orxtaWP79133w0AGK8xBW6zmSTOv/KVFwAAn//8nwEAnn/+eVfm/Pnz\nQXs5j7yPY9On28OMJVmJvhrjnD4q2diY72oXf2/Zthiquj2Vbkqrla/S8sA+av/TLv6VW9sbwbWp\n9K+x71gHffjC9qc1yFOfU6NTl9FjLf3mUz7EObTtF7Hn3cWnWJfV+14sviOVIturorSnDU754sq/\nU37ZsT0/NZYdtspOuB1qGdhDSticpfVria/n/ZgaXscGuP+r3BjIWJEwlkWLXPT7/v+poWVz6adL\ndlfPs53Na+4a7t9aP9vPr/C3U9ZHAvfll5/707qu35Tru+9JQUFBQUFBQUFBwQHB/vDBhfnvvcuJ\nSmepirG0KcZiGaUCooviQgo5H9yUr6xsRxtzm2edujMXKVUA7RsKNFlYp2OqfKT13/Iafh7zf0xF\nD3fRZ0w977bP5OddIo67sB7zaXf2LDUvtSpETLe4LaK8P5SMuvGPonYhnwf9pmQbycLxu4cffhgA\n8Pf+3t8N6tjZ8XTs1atXAQDHjh0DANx5p8k4xsdL/9rRqDnGH/uY0Vd973vfa+vdaZTR87+qGKU+\nCfoj2Uz63M5mIQ2RsnAAMaZwHr3WtCFkglPzSEacN6wS6E6Fpdk0X4det9oKlrNSkKFfJjtjM+ah\naYFIKbTkoHVWU4oz+WeX7muKudWfx+Imuqjf6LJ8JYMV0w7Xygv6945R6bnn0J6ZLb3Pco1JH/hU\nxjpmjrxV3A4jMnWtm3W3P5evF/aTWoNnZzkH5bfc78L/sTitZjM/N9z4OmuKsQKuWq1w7sWj9WPu\nGs6tnV1j4dMZ/hjHEMS0uPiK5TKjFQa3oKCgoKCgoKDgQKH8g1tQUFBQUFBQUHCgsG9cFKqqyppz\nU+bbnFO8NmkuE2yUC2LrmjYuZkbSZvuc6S7lxpC7V8pkHWtzqi2pa2P16LbFgsxSwVK5azRyY5CS\nB+si7q773CXIrItZchBJQHKryPUn9ZzlNUzKoF06GIhw48YNV/bMmTMAgDe84Q0AgL/21/4aAODm\nTeO6wGCC7S0vLXb33eaac+fO2/sYs9SRIzZNrjVdb9z07gef+pRxTXjPe94DQJiWrSVKzo2FcvuY\nW9maPtN02kCu2cTXT3MYX51EXYc15ceQriLpQM0uc6/tPs1gi+7X5lINd9k//FoNTYDLBeKEQV85\necFbMdfqPaJLsp9bQSxIS98nF+SsXb60+4G8lubaCxeMtB4DK5nk5JCV2Ovm4pHeL9qCzOIuEKxv\n//FiMXePrmiTy9wrvpEuCQTnGuXA9G8yV46cvX487Afu94L9kestlBRzyWXsfksXt7U1n953OB4F\nr6srxp2BLguUqwyDR6dB27pi/83UgoKCgoKCgoKCglvA/mBwqyqZ/lIzFCmH+pzTvT7xx+R+CH0S\n5GuXIDaNWD9SMjAxpNg5npZyslFdAqHagvBiTKUebx0wFjthpYIecil09bWp5yL/TiV8WEZSLNWO\nHJYJZlvmXimWpUtbYmV18BHHi8zuqVOnXVkmbeArEz7wefPaixcvNuon+8skDowLsCQCPvCBD7hr\nPv7xjwf16QQPi9qf4gfDkAHjOiBLMJ819wK9HoZD0/6dnWawDpFeM+1s6XJBVJTqMe8Xlg1Z1E02\nKvns7cc7k91G2dQ1vX5aqs42YUnrUVzOK9rc1rksA2eXDwjVZfZi1dHoIgvXJUiVgZM62FZew6Cc\nVNrx3UlTY69tr4/No9hvn7xf7Pc0Zw28FdyOelLPeZlg69uF/cDcEr5v8YBQ9/+NTF6jZcJsgKzb\nBSvxu12FvyVMBsFAtN3dQfAe8HKUtAIynfxq30iNcV3wN8fUw/aXILOCgoKCgoKCgoJvYuwPBlch\nlsCg7eQfYyZTrGmMgU3J7sRY4LbT4TIMRo7xSZ22Y+L+yVStGQmZ1pR5kf7oenLC6qnx1/2JPbs2\nWaEuiRJi9cf6FHsfQ4qty5XtwhCn2tv0lW2eR9sYNilxtLpq/KB2dydBfWfOmJS3P/7jP+7KPvbY\nYwA8u8trb968CcA/w/vvv99dQ2aVzC2xu2vmyO9/8P0AgA9+8PcbfeXJn+mDOa/kXGFfeMJ3SRsy\niUMGg75tg2HPYkkmuqNSr95PzavlaMYkLUPWZnWJ+aanmNUu6yG3h7o2Rfud3580ugj3p5nv1ks7\n1dulz132CUDLzoX7XY7V1PWRsWJZLXgv73X06NHgWs6Fcy+/2mhfm9Ugx8Z2kW9LjeVifpuYyttA\noGpCOu9LHGczDyK0lYD7U9Yiyj0GHJ9wL6tr+X+HfW24Z9uyNksEfcsBP+/J0DbSzdv7yXXH77a3\nN7t021+3VOmCgoKCgoKCgoKCfY79weDW5jTVJSI1dfKMnZzbfIZyosE5hjV14uvif6f9H3XZGGuT\nSnmbEzjX79t8r4Cm6oSO/o21JccQp56RblOMVdbQpzw5bmy3HpdYZG2KOdKphnMWgdS1OaZb1xWr\np41JYHQp4P2YvO+TYYHW19cBeH+/tcOHotcDwOtf/3oAwI/+6I8CAA4d8mV1VOzly5eD90escPdg\n4Puzujqy9zHvLWGFd73rXQCAJz/7pwDCVI+eqaV/ubnYzw2/Rjk887lpw8j6btG/K/YcdnYoSk+W\nF40yKaSeYWzu+TbGrRa8v/k7nAPa75jIsb76tUsSkFj7Nfh8yZ5ogXV5H79HWmUKFemc8/dPt8WX\n0/fWbHWOpdN7V4zpTu2RLMOxkM8nNaZdBOjJ4Or7xRhi3pPf3XPPPQCAtUNmfV+6dMldQ4UFossz\n02OXU49J/o7W4VyPJUDR1+biOtp8e2O/wb5sPGFSF8tDzKKb+n1exu+7y/prK3ur0NXxvV4X4fO3\nz8NdbNe3q0u2n/truN4a/z8J25BOHNKjCk4vPdZkkZcdn8LgFhQUFBQUFBQUHCjsDwYXdfR0JqF9\ncXN+tqxLR43n/C6XiaZv83XKnR71ST+llRirl32MRd+m0CXyVZ+2cqdJzQoswwTo/uj7S6RO0DG2\n02mbZvyB25Bi3mKfdYnQXSTYoWXaRJaWfrCSYdA+qvSR1X6q85m/36BvGNbv+I7vAAC84x3vAODZ\n2b5IvXnxwuWg3fS9JQt16uQZez+vcjCbhXP5l3/51wAATz31FABgOGqy4/7vOLvfBc101/671FzI\nqwLk13Fsr2qrX/Yn5b95KxHfOQtWrEyqrpw/s66juUbZD3eHRtkUAx1jeKfTuC7wXnwnycbKa7RV\nSLPhZFFje00qVXzsOafY3dxenIqLOHnyJADgxIkT7hqnsGBNJ9euXQPgU2fL+5MR1lrFuVTvKQvG\nYMjnHTL5sX7oa2WdI7svaTTbJtedKxW9Tw5dfK9TazK3hlK/IV1iNTRyltYubfGfxX+7/PvmnqzL\nuufNOhfNOS7uyC/CT8Xy5lepte/noFw3pqzW7m9DYXALCgoKCgoKCgoOFMo/uAUFBQUFBQUFBQcK\n+8RFIU3jU8RdpzdMOcsDeXO/LLuM6WAvZoaYuUoHQuVM+ylTSiwVpi7TxRTYNO22y6ikXBR0P7sg\nF/jRFgAiocelyz3bvo+be+LmsKg5Tl3exWSt31NaRZtSAZESUQXYsV4GmY3Gfg183/d9X/D67LPP\nA/CBaZubXljbiXtbUe+zZ88CAH7oh34oaNNY1H/9upFw+T9//VcBAFeuXAEAnDx1HECYCjiFZdZD\net6my4hvGmX1fbrMZd2mlItOPtili3k1XyYevNj9Ps6tq1KBmq7daVetVPKXHLTpNIe29ZYzTy/z\nXUp6LRcYmnJZALoFnqXamHJR6A8onO/3On7H38rDh41gPhOubG56aSW6MeiAU9132XYdfKxF/Zf5\nbYwFpKXK6v1W/p63uffc6trtGvQVdW9Avh+x63V7uSfnrtGI9llN3bT7Unvymtw9UwlPXP+qZrKU\n1DN0c28e2SOq7vsGUBjcgoKCgoKCgoKCA4Z9yeDKE4Rm5XJJCAh9StSBE8uwjMugC1uQcqjulhLT\nfEeB5FhgSYpRiLGAWrIs1YYca7qXk7LGMsE1uSCk1En5drSxy30kOGYpBrrL/fR9pCA8P2PQCBkZ\nLSv0wz/8X7trztxhUvG+bMXi77nnPgBeZujqVS83tL5uJMNeeukcAODtbzcBaV6ay6zLjY0dd80v\n/dIvmfpumOAWssgMknNBbJlTOB9Vl+ecYm5zwaMMkOj1WW87k7sXaSB331oHXsFFyDBmpkb3fSll\nuepiEcitC443508ykUTdlxWwA7ZMYgyi0PtgcwxS0mI5BrctOC5mLWIwGZkjMqEM3JR1ct5rC0oO\nqbnRxQJHuECyna1GP3Rwk35mZHQBLwW4jHUz9fsgpe/a+pML3MwlHYjVJfvWFqCZt5yEkPurrr+L\n1c4xzVU+QC0eZBu2N1a2SwIVjd4gv4aWQaxNfk6o+6rxkzu+txbFGdwcy8/96fOf+3SnNhcGt6Cg\noKCgoKCg4EBh3zC4VVVFTxWaWUhJQsWu7eoDJetJfR5Li9vlpKmhGWmWpd9ajC3QovsxNiIltJxi\njCW0j5U+qXWR/tL9Wea7nO+TZgBizF7u1Cu/b/ss93mu/pyfVJc25XyeAc/OSiZGp8wlM8MyP/Ij\nPwIAOHLkDnfN1Ep6nTppPvv85z5v3p86BQA4fvy4K0t25i1veSsAKZ1k5tPGhrn/r/zKr/g2bRgG\nWKdxJiM22W3KLrUxI7lx6sLgegaVn8XZzfA+3S0AjbVh71fRD69q1tGYy714m7owx8t8l6u3OYbx\nunOMkvOFn2aSTvSa+2kKbesst+40CxSzTunfkpQFK5aIJpV8Imft6hJ30WbhI6Sfs05MoX9jZCIJ\n7heSrZSIr4fUvGm3PKT3Zl8X02l3YXv1vfnaJm+Xg/4Nlp+lkopk2diWJsTWUMpqGqs/ZSGJroc6\n8V2HYWqMZfPROX/jWlVIdrbfyOXbbiGu66Z1gfdZ5n86c11BQUFBQUFBQUHBAcI+YXAr9Hq9TkwY\n/4PXp1TpU6RZzGX8pFKnxpjfSaqNXZA6xcd8k/R96BsW67NGzu8rFfmYS4XZJfVsCqkxTflf59of\nYxi6iGOnkBrrLsgxxKnX3HzS4BiTtQV8Egj6uT7++OMAPHNLBYbr1/01XDPnz58HADz44EMApN+l\nX1N33nFXcA0TPJw7Z3xyf/M3/w0ALyoPANs7JlqbbDLnKaO4B84HV4yt+5vOt3Z8yKLGGNwGa4qw\nbPA8bH2qDvp8xtOMptOKmu8jbASZ2yos02Vu9BLsUOya1Od7ma+xdddcB1zfsb2ZrGXnW7tx0sy6\nfx6+TYNhvE9dnkeXceF3tDAQXG+pFMqyzDJsb1s7ZL0p0Ade3oepqr0153DwXv5WuvSovXhcimby\nzb3i60A3tYu/a475TPkd5+ZrWx3LQM6DlMUtN/c8gxufe7l1nep7ztqlEbPaarWjW/lfJfVefpay\nuErVlOaasfFByIyXW29FRaGgoKCgoKCgoOCbGPuEwTXI+dzwFEo/Gfoa8r30KeLfy/prAN1OOKnv\nlvH/0af72ImNJzEybNKXKlVfagxjTFWuvr0ix0Z28Y3VZZdhqFK+bl3akDqB5q7JndBTvnNd2Dl9\nEo+lAaVf7g/+4A8CAN72trcB8IoFzmd24us+f/4CAOCR1z0MwDM8ZHsfe+xRV/bqVZOq9/Rpkxr0\nS1/+MgDgN37jnbZ+wxpR0QMAKutnxTaQySX7u5hybP26bM6XdqZev28+5rRuqWcOQy3p8D7dU236\nevfu+9eFIckxwvrzrv7fubiFlBUnvi5CxRCt9Z1rbxfLzK2wctpHNmf10uwpGdxcrEPOrzal5pOK\nj4hB3282b7Y9FTPBPUL+Durx0MhZUXNsderaLvNVX5+bc/p63ee9zBXWJX1wc37Sqfu4NrWU7dKf\n3H1S13Rhx1NzL2qVSt0Xzf+reqreXo/3M9/njJSV1Vho3LVOa+d2RWFwCwoKCgoKCgoKDhT2CYNb\no67rKPPG/9h5Yj1x4gQA4L77jIYn/WYuXrzornn55ZcBNCPNc0idcGKn1zYVhdwJiKxAww8vwjDs\nhYHJR56GaPN5ykUEa51Xff8YUkzlMn5GsXLLMGGpa9qYjS5tCdre0pYu/eTYsk1kQgGfUey7v/u7\nAQCXLl0C4H1yyZ6ePfusu+bbv/3bg/qnM8PwPPDAA8G15nqTSeeznzVKC//3v/0tAJ6x1QoJplMh\nU0VmeDg020y/Z5lcMWUWtc6GpOeT/FvPbeW/m0OdZixSWMbisEyZ5aLF423p0rZl/O18JH7IWnax\nijTb0FyPKbZJ3ydcf3FWLsdG6bHUjHQsy5q29HH+xpRtlnl2qb5pTXK5h2p1hkZb0GQW05aN5p7G\nv1MqCjGkmNU2VjB3Ta5+3Y/c3PP1du5OJ6Sebycd88T46LqBdua5y9xbph+p3+0c3H0jzK0uk35N\nW8iqKlyTrs2Qlj7z2l/y97kwuAUFBQUFBQUFBQcK5R/cgoKCgoKCgoKCA4V94qJgaOkYpc/gMppq\nmFaUrzT5SykUXk+zDstQWonv5X1YlvXowAAZkJULDGNf5Ksso01m2lwVS8GYMhFJ05oOaEiZxaSJ\nTieO0GLZ2lyWa4MOoJBt0eYRbeaR19B0ps3zum2x5BCpAJkYXP0I+04TvEyqQAd6mvBZ78i6DMTM\nPltzI421vr4e1DudmDZSQgsABoNxUP/AzrXp3PT1yAkT6PVX/sp/6a553SOvBwCcfenVoF/9vmn3\n088ZN51Hv+Uh/+VgFtznzBmTundRhalKAeCLX/gCAOC3f/u3AQCzXXONc5OobBKSqXdr4BqZTHbt\nJ6GpbnfaTDNaW9PVbB6uu5x5feHmXkIqUJjDtPnZr/NQPkenHQWa6zgfvJjfC3JgUohcn9vcDGLu\nANosnEtz7taDFd2nStvcmhOdalvk3hzj+cK8coznkTbRxFg7s6e9f2ScBtZ0uZiHwWta/mo29evO\n7Re9MDGQC+qd+fu4PdG+usBMJa1UV37c5rNwj5kvuB82Xcx88JLaV21zt7bN2pHuAuyj+73hc7fT\nOJfGVLuT6P1dtjuVvr5LcGdzLnLeNl0hdB2x9vN3Wac/9m1tBi26OefK5ud6DjEXt5QbQEpaU8JJ\nyLkbNG7o/1RfLlS9XFPyM/c7qn+va46t2P9U4CfXZK3+v4n9brv7ue+aLju+3Ywm47oO9zKpuFdV\n4f88NdT/BaodADDjPl5cFAoKCgoKCgoKCr6Zsa8Y3Njfmp3TyDlpsx6eEDVLG2Mw9H0d+7S7myyb\nY0Z0fWxLqq1dAjNyTFKqLTE2NoVlynQJNGgLglgmsC7GAMRkXlL3bzAVbLc9gTKIUT4nPfc0exCT\npRuvGabzxnUjmaUl32TA2NaWYXLIVHA87r3nHgDAT/zETwIAtrf9HHz66adtew27u7Nj2vDiiy8A\n8EGYDPQCPGt85513AvBEAhMxPPXUU67se//jewD4eT+whclEx1Ku6rXIfpAxZv9iAYIxJiH2XiLF\nsuTYp5T8T5egxRh0AGsXRqwtaDTGDrW1pQtj1WWcUgG5XQLgdPKDLgFFubJt0ocxq5eW1NNC910C\nfLoE5XXZV1PPVc9BeT+Of8qapi1+sXp1XbG5rfc03daYhKRudyrZhSyjryViyQhSkmu5uZeyCnYK\nBrOI/ebocU6NcYwd10moYmVT915m70ntMbF+pMYh9wxTv8XLrI9cQNwyEmxtY5tCYXALCgoKCgoK\nCgoOFPYNgwvkTy2audO+RDkWUJ/yYqcWzYDkGJFUfbofsZNUqj85nyp9nxib3Xbii52GJYsYu2+O\nVc6xEKk2pU5+0getjaWJMUy8Rvuc5VgV3Sb6Qm1ubtk2yTSdikVJzBU5D7Y3DdNJuS763o5GZsxv\n3tx0Zcls8nk8/NDrAADveMcPA5DyXYItsIzzKy+ds/c2dTz68CMAgBs3bgAA1o+su2vITtNHlm37\n5Cc/CQB4z3/4XVc2ZaXgs+pHGHv6tvf7oSSTryvNdul10IUx7MLO6vmZYwx1PSmrQReGOMdctLFN\nOV/c1P1ylpNcW9rqz/U99Rxi1pyuvpFBnxMs+zKskL5vzPKjYzZ0/fPIfbqw1roesqK5fTFlbczF\nfWiWN5fgKOaXK/vBNkprmO4j28C9IGUVkd/l5jTRNv9jc0//Jurn3IVt1HXKevS9u0hpLrPeUms/\nZ/lpswjk6texRXpexfqmY5ZiFoEUlrFg5eZKro85FAa3oKCgoKCgoKDgQGFfMbg5H5U2liAmZq39\nZ/mep9iY/6A+IcR8rNpYiBxzuAxb0+ZftAxDGWM+Y77IbdCnLd2WmLJDqu85Nq3txJZjlbuwBY1r\n7XutoiHh24+gjPbzA4C1VaNmMNnl6ddce/O6ST5y/PhxV3Y4NMztm9/8ZgDA937P99n6zfdXr161\nJX39r75q1BMeftiwvWfOnAHg/Wnpgys1tKk6wH787u8axvYjH/mIaceoaQVhSt5F1fSfBUKfKD92\n4fP1c6/9Obf5XQLx1MXyfWx9aqH/lA9i7LNl1kfq2pw/rW5LjilJ9Tm3HjSW8Y3NXZtiubqs6y5r\ntkqwo7m26vmk50rsOaTmDefzVISA6zkdSwahrydSygWxNvFa7d8f289Tzyy256d8kvnK+8ViTjSb\nrONJcvOqC1K/VV2sBzllHo2UFSQWw9Hld1rXk/LLjyG1nnPrsM2fVkKnlE71I2dBzn3eth/FfL71\nd6n9ai/7rkZhcAsKCgoKCgoKCg4U9iWDu8ypL+ZDkqpPnxxip+C2KNYccsxJyodnGb/jVJ25epbx\nNdyLv5SOts35yKTqz/kU63HrotKQa39XprCX8UnSLEvMIkCtS7IcjvVYNX6vs7lv2w98/18GADzy\nyKMAgAsXTNpd+gOz/nPnzrlrvvVbvs30w+pyPvv0MwCAEydOAQB2eoaBmfc8+8Rn9cEPfhAA8OEP\nf9hxQCTKAAAgAElEQVR87phbvx48G2v9+pRe8GxumB45xvQhJlNMNkgzIzn95ty64L0ko6bboJFi\nS5dZz6l2AM2+7WWda+TWUBfWuu3eS1k21P4nr9UR2l362sY2yvqnKp2sZshyPp9O01b5D8r7pjTJ\n9fqOPY226PrYvbke9L4X9FmlctdznZD7bmofje2D2gcz5V+bSx+cigW5VQY3126Nrr71MbYxxRR2\n8e/U1+Z8V28FXeb4Mgxrao3yGUprQ0oNR98/1pbU2o9ZE/by/96yKAxuQUFBQUFBQUHBgcK+YHBr\npP/zb2MVY6cvXWY0GgVlcsyePrXoE69ESpUhd5LVkadZH7TEqauLXw7f59QaUv6zy0S0a8iyqTHV\nYylZitTzTX0fa2cqchRosq7aD5lZkWIR4GRNOJ902ySbt1hsBdewDauWwX3LW97oyp57xfjTPvvc\n2eB+KytrAPxzOXbsmLvm6WcNY8uMfvfefZ/tz4WgLZLBfd/73gcAOH/+vK3fsMvbO8Zv9+bNm64s\nM7k5xhMhU8j+SCZpbc20lyoNnOueuUpHsmvk1tJe5qVm9PR9YoxVl3XQ1Ucvx6roPUHPTfl3SjM0\n54e6DIuc0i+N7Z0pn/qUlq4sq19jmRbHyjqk/Wlz+3ksW6Vso7xet63BZooy2rdXWwRibGzKX5SQ\n48W9RUMzrnJu6H0854/KMtSzTv2GxdZoTINXfp+zXObWiWaCl4lT2Yv1IPV7vUz7c2X1XOjSfo1Y\nnE1bW2L3Salm5PzP9WfLaGwnLaKR+rteK68pKgoFBQUFBQUFBQXf1Cj/4BYUFBQUFBQUFBwo7AsX\nhQpxM4r+O3ptB/qfkie59JzaTOKCaSzFL81IqQAooosJtS3AK1aflqWK1aff51wUaBrV39G0FpPM\nSgWI5VIjtkmTxAImUk7wWrJG/k3zXsoUJcs60+IsHP9cQgkvNRXeh5Dm1eGqMddvb18BABw7ZmTB\njp82pv9P/elnXFkGhnEcKAvGxAyVDf66du2Gu4bjwLS751552ba7CsbiY3/8UXfNSy+9BABYWbUu\nFhumz0zhK82jN25cC8ZhZ2pcLpiil32VpqeNDZOWeGfHmD8pWeafzzhoOxA3kQF5M5V2K1nGjSX1\nKteUdhFIuQXIz1JmPS1PJv/W3+k2yTmu16RuU0zKahlXDm2G7mL6TQVa5cy4KXNnzBzKx9jmehQz\nf3I8UskVJFLzh2MxHns5LD1fci4Kem/sEgybGh/OEfYrtob0OMWCX1NuJDkzccqFbZngzi4BY3r+\npOTCYmVTY7zM/xJyD2gLzuriltFlDNpcL3P1p8rG3GRSaexz6ZyJ2NxOtSm1v+ZS7HYZr70GohUG\nt6CgoKCgoKCg4EBhXzC4gDmBxf6Tbws6iiHl4KzZOclY6RMGJY9izs1dgzZi36dONjGGNcUk6ffy\n77YgFNkmzRZodiLm0K0DxciQxBjc1KmOp8oYG8GTpj5ZdjnBsb5UggyJHhMXDMJTr2tT1Tz7LezQ\n1Yt4EIGMRllMzTO64667AQAnT542/bL9G4w8K3TdyoG5VMA2oOuqYGwBYHN7y/29YtnQK9cM0zoa\nh6k9n3rqKQDAZOpTArOdJ07ea8ZAjQ+ZXAA4dMj8/fzzz5t6d61kU2U6ub6+HrQZ8Izttm2nft69\nXijDJO+tn3dOIk/Pm1zABKHXm14Py6wliVRwp25LjMHV6TK7BKXotuQYXP2+yxpKyafl+v7/sfcu\nsbYk15XYzsxzzv29eq/+xU+RKlJks8E2IaoFSd1oyGho0IYMA5Zn7YE9tAeGYQMe2SNPeubP0EAb\nHhowDNgDwxAM+AejG7ApipJKVDUlgaSKVLGK9Xv/+zmfzPQg9orYsXNHZOR9j/T1rVjAw3knT0Rk\nZGZE5I21915b38NpiuaA1Jpj9VH3NzWfpQUo3FOwjGnWMcVAh+czvY6SsZbqfwm7lXofpO6b1X99\nfbn5oN+JuHY5XueCyyzGVY/hXMCmDuhOBePl2NjUvLDWmtTfFHLspyx5uaBLbdVcEviWGtMl8qe5\n9UmnYAZSMmvy/6lgv5xUZ+pviBwTnSpjJe8q+fsvamNR6YqKioqKioqKioobjhvD4A7DkJWsSEm4\nACVSFtjFaF8+Cfx2dXUVlckxkylYuyJ8anZW+9hZv+ldkeU3uERGQ+/iUr5zFvOjr6OEbdJIMQNE\n8yLiOampnRKIl7vwro13sv58zJquj0+S7cMXNvjtujJg+9dHwSLw7d9yaXfPzpwsGMbe9mr6nF9e\nu3qQBfMMErPI5xdOvstKd9hyH8Ce/vRv3iUiooePnR/v8VGY4t/4xjeia0afIBcG/12iICP0+LFj\nkcHgginG/ZHSYpqRQsKHwN7Ez4VoynBq/1QrgUEJA5P6rWR+aEZKS6Pl2kgxS3JMyjSoRFP/OGs+\nzPkCWlJ7S6wfqf6XlEX7+jrkeS2mWWKJFJF+T+TYTO0HW3IevTZb1zHnBymhmTZtBTPndUbqUPZN\n19f91X1K+ayW+OBqpNhfq/9zbVllcu/ZVDs5K2SJVVND36/c3NIMbqqvSywppvVR9SUnC6fHmv60\nmOoUa23NNw2cW1t2cwxuyp/dsmYvRWVwKyoqKioqKioqbhVuBIM7jmNyV5BibjWbY/lhpXzEcoyP\n3oWBcZAMTMqfKNVH+ZtWdMj5JuUUEHTZud2odTyXgi+F1LVabGxql6ifi8WQzEWyW9C+jZp5tfoN\n5rZplBKGGBqj7x4OxozVEfuunp3d8XW+/4O/ICKiU2aEMT7DLjXca7Sz6WL2crNydS4vz6M2iIhW\nzLaenzsG9dNPPyUioidPHeMKVvmwv/J1fvzjHxNRGMubTXy/5C4Zz+TiwvnTHm9i5laz5PLawjOK\nn++SiGPdD6JwX3Btc/NQ/j/lB67L5fqm2UD5/5Q/WUk0tGZ0c8xriX+tPvaLYnLnnlmOBUz1zbqO\n1Hlz0fwoo9Pj5pDyNcxZ+vQaZ61/ep3N+ZZqBlJfayoRhETKH9Uqo1HC4Kbuf4mYv/X7Ur9KE2Oe\nkc5WLfJRn0/t7dtLlM2pZujfcuoG12GVfd8WsNZAzk8+NTZSLLPVvj6ei50pSbEuURncioqKioqK\nioqKW4WbweCS2yVbu4uUFl6OyUtFCKai+OVveuev02hafUil9rT8f+aYhBLWw0pDeR2fp9Q91Iyr\nfAb6PNpn0to9aj9L3UfLlzi1W8350GlWroRJmvjDsX/taiNZ37j9rmMmkdPutqyLe7kN59+17jom\nqZmHqb/iHfbTBTMJhQSdohcatEREH33k0vt+/KFLzbs/7LhvuF98fRTuNdjecA9iNkjeP7DFL754\n17XHx3PPLsXUti36NFVR0HVz43duvllIMau41xbmWA95vpSW9BL/wdSaUMI+lbDjS5jbFHJ9WdLP\n1PEcyzxXJmfJ0mo4JYyY/ixRUbDWP/0bfN1TKdKJpiofc5YHec45plVCs9JL7mnJeXTZ3DOcezeW\nMJNd4r1gPY8USuZqETNcyFDK/6dSJlv1U32x+janVW3rT+eZ4lyMgD5vjsFNXXtuLi31xa0MbkVF\nRUVFRUVFxa1C/QO3oqKioqKioqLiVuFmuCgMA+12O9PUkZLLyDk++3b5N0h+pcyVsr42S+Wkh5a4\nKKScr7UMTE7+KuUWQDQvSG2ZPLQge8ocY4ngA7i3ufufCsjQ1yXbnwsqs8xVCMoaSF3raO3j+Dms\nWOKmc+b6fQcTYSgJc/3Lr75ORER37tyJrifc61DnggMNcGYd8CYDxo7ZjIpEC8csG3b3rjsPAtUu\nLp/6Oh9/6FwU1hz89dLLcCVwncBzaWlq5tHmHtxj6TYR5p09vyzTUzqY031eXl5F55P/LzG36TmU\nM8FqpEyLOfNnyqRpuVClTJYlslRLpP2AlElwCXJuDfo8JS4EQIk4fUlAiXZp0mWt558y6ZeYh4ES\n9xK9tlnvC11GpxLXfbba12WsNXku2Nl6ziWpsef6pJ9DSWBaiauIRomLQqpuSeAbPq307Km+ZMu0\n9rXmAhH1O90KJpxz0bGedyopTu55PIs7A6DLyutJJd5KuSrI/9cgs4qKioqKioqKis80bgSD27Yt\nHR8fmztBnZwBKAks0TsQfNcJFOT/U+lwLef+FBOZ25GmGOeSnSHqWvcklcoxdx7NtOm2LBHo6zBF\nOl2gZmasQKLUrjQX0DAc4lS3bRefT/5/bJWEGSeAQLIGsI1ERKd3HDv65psuxS2CRR4+ckFbkHmS\nqW5fPV5H14wdLM4ziJ0o6u/4nGBfH9530l+XF465RZAYEdHTx4/4f3x/epafO+yje9J1gSnWzxvn\nxXOQgVd9H4/3VSJoIBd8mWKdckwPkGNttERZSZDTEmZyLi2kPD43h/T5LCwJDNV15tiv0vZ02RL2\nKWWN0gkfcv3N9RsSdangk1zAj7Ya6esjSktx5ZIspO5PjlkFNIO7JGhHty+DbVJrZo5Jx/tsLnDM\nwhzTarVznTGeasvCEqk93Z7VxzmLVdai0caWptxaoJ+ZHnMWW6oZzyV/O+jrsOTI5hhc6z7Nra8l\nMmEaOatOKSqDW1FRUVFRUVFRcatwIxjcvu/p0aNH5m8pXye9O7V2CKkdrbUL1oxtkRwIYy4hg4Ui\nX57ELtJKIZnyZymR8Uq1odOnWu3nUgrqOmAzNQNd4vsE5PzuDjvlb2cwuP4e8mP1vr6czeHqyjEB\nd+/e9XXA4OK+P3rsGFWksw1jJ4yVVc+JHZi1Qd/8dRxCWTCSW2Zwt1vX7qOHD4mI6OKJS94wUhiv\n6xV8mxzryl/pMMbP/XIb6oDlmI5pJG8IrLVOarDLpGT2rSzw5U4hx/ClWJMUi2CVSfU1x2DkjqeY\nqZzlZI7tzSHVbs53P3XcepZLrFIpZif3nOeeocR1EsXotURb5iyGVV9HSs5QnjOFHFOFdaJE8lCj\nJKlFKibEko3ylqwC3/fU9eT6VDInU+1q5Obo3HmsuZq6VjmH5nxLrb77/zfpBBgppOZQLnFICYOb\n8kFPtWVBp92Vc2zuPuE4UtZbSD2PkvVpDpXBraioqKioqKiouFW4EQzuarWiV1991dwN6J1ZykfW\n8uvDJxiykjqp7yXM5xIGac7Xzaqjj1vMhWZdcwwudlXX2cXjM+UjbV1jSsy6JJo71Q/ZHnxj/Y58\nnPpsgsU8Z6Z2v2NmZ4TPk7uPdwSDS+wL+Ih9bg88fsLOltnSy+AHdjh3O+d1G3xgiURq40b6Mrr6\nh/026uPu0p0XaXk3m9NQh5Ai2V3rhp/zeqXu9Sr41R4fM5us2Fh8h++v1d8StmDONxI+iNa8K0m7\nq+uk+lLCGuWYgNRcLenTnE+xVWapeLnVx1z7JcxtCin/VNmObk8qhMwhdx167UqNQWst8JYHlYjD\nYpfnWC15zaVp2uWxJaxZaqzpflvKNikLpcXgpphPaxynxnTK79U6dp3xWjLvRItmGxZS7eYsiUvW\nmnGM54q26OYSQKV8u2XZlNW0ZK3ENeba0n3Qc1/O79Rc0e/8JUoxOWvzkrWLqDK4FRUVFRUVFRUV\ntww3gsFtmoZWq5X5V7pmW/XOBj4mkqXTqXjBBOiycseDnYiPdlf+XiXRwyVsbIrtzUUpA7mdZsrH\npkTvcG53mmNIcj64eteG9rT+quyjZh/wzPBds/CyHejg+p05xXqvsr1dz+du3Njo1qwn+9LL7vdD\n6Dt8bZs2ni5XVzu+9n3UBlHYOXbMqK6ZSWVZ2eh+QVHh/KljRB5+et/9MOIaeZc/Sh8x9tHaM8PD\ner4tn2DD+ri9eAY6ilv7eZ2eBoYYigr+GapnZe2yU76AmoXKsRJ6bFjj1WKmJEosKTlGIcUWWEzS\nVAt5uf/9UlaipM2lZefWgJyfokZOGztlzckx6nPMrVwPtVKLTsmc0yDVa741DvT6U6Lbra9tCYPr\nLT5IJW6st5oly7G0eu6X+LOn2GqsJyWMW4rZs9qfU2Ox+tIf4jm/xJpagkVrTROPgZSeszyWYjxL\nfN5Lrif1d4E1h+Z0aS1VolR7VvsppKzapddooTK4FRUVFRUVFRUVtwo3gsEdyf31bu2GUv4fuUwZ\ngN6NpnYX8jx6x4wdSt8HhnjaT3t3FO+C1Y68jX1X7YhXO9KVicosW6DbtcqmNOXmfBxlGWTKGvn6\nesEyenUDxcZqVlYCv+E5XzHbDo1ZtN7L3Tzqcvaw7ZZ9rgewK6EsmNlXX3+N23V+yMcnp9E1nwq2\nFucCw4ldITKaWTjanETtQRuRuE+PHj/0Zd9/72eu/wenzvDiy451wkYfrK/c+a/UjnzdxSxUUD8I\n6g2+L3wdmjHJsfs5X2sgxeDqT4tRx6e2DFh6n3Pnt46l+m21ORetb60bQI79033SLM0S33fdlhwb\nJX7Ac1jCSOvzWWxNip3L9W0uYtpixGCdSzFLFoOr1V30HJIssJ4Pfk0w1vxU/Ii2ZFl67NqSoX+3\nkFIWyrGAqb5aLGOJSoNGahzJeT2XDTDn3+z72Krvqu/6/7I9vJvHyEJWNlesUnhvp6yciBWRx3A9\nGGsW85lS99BMuhwjc1Zm695qpraEfZ3zk5cW1+dhsSpFZXArKioqKioqKipuFeofuBUVFRUVFRUV\nFbcKN8JFgUZHlefMeSnzheVukKLjc8khtHyGNiNtNtOUpzB9p6RcIDnl2lcdb3CetGTWOMK0oU2m\ndnrQ1LHotNcIMMkdG3UAAoXfUy4h2gQiTSow/b3wwgtERPTk/Dw6H0yC0iUF9/0pp7J9gRMz+Ocu\nDElvfvHLRET07d/4u64Mm/ZbvscIFIPrggTMRzg3XBS864hMKEFsdhtiN4y/+ZufEBHRH373O/7Y\nyy+/SEREr732ChERHTjhwsB1W8MQtlImIS9B1MQmIstFASgxQ6dcaKzxqk2Yum84bgmFp1AStFNi\n8poLppG/azmnnCvHnKxZzlUB7cOsngoisdrJBRKVPKtSWO3PlbGkuFL3YUmgTKqsJbuENUW7gcg5\nirVGm9y1G5wlyQXkAn7mAotzAT5zpmVp4tfzKtd/1NNSbtolqCQV8POS2ku5XaRcnaw+6fNY/Ui5\nZ1hjI9Xe3HeiaUIPPS8stwPdJ8slTLsoaHeZ67gUWNeRuk/6d6svc65U8v8la4vGErkxosrgVlRU\nVFRUVFRU3DLcDAaXxlkGN5UWcEnawyVyGnonstl0kzKQiXry5ElUFgkU5PkgNTUngp7rY0kwSmqH\nmbu3c8gG76i+tRmmCsA9QBvynkBiCI74ayXzY/UJO+M3P/9FIpqm5/yX/+Hv+rLf/DvfiuqPzLKv\nV5wEgYPAjo5OJu2DwdWBAFcX02e778GAuGN//dc/IiKi7333j4goBJa5a3XnPlozo3c18Pdp4CTQ\npdg+vi0Wqwzo8WPtpPV40RaOkrTUmv21ggpT8nz6nsv+peTnUt8t5AJbNBOi+y+ZJN0nXabkPs2l\npJXn0ayfte6lZNpKmNzrpCoHcqy4vo7S77KdORaeaMrs6eBjiwXUiU5SYz/VvxT089SsXE4aMiXV\nhPmQu0+aMbSC2HRAnU4MZMnBacuGvl/yvqWCjaz5oIOw9Ji26sxZKay1omQuppAap9ZzQEC0vgfW\n+oeyuO8oY7GwKdY1FSSZ63/u76Yl62nO6jSHJdJr17VCVQa3oqKioqKioqLiVuGGMLgO1l/pKday\nZKeQYn+tnb/eXWHnj8+vf/1r/rd33nmHiIh+8hPnT4ld2EsvvUREkgUJbAHKaH8jXEWegVZyJpTe\ngc7dJ0viaM5vJruDo3T7qWMpRtc6p97dW+2/+KLzYT1w+t277L/723//7xER0bf+pV/zZZGid42k\nB0iTe3DnOWHWdBjCswOTenLkWOWg+OXu3/EGjF94hvu9q//973+fiIi++93vEhHR/QefEhHR2fGU\nmQQjcveOswAgFaaXghL+zSOBHY3vhb5/uXtbkhYX0GxsibSYZqHu3buXrLNkXqfYUYuhSV0r7m0u\ndav2YbT6rf2NNZtiMSX6vug+XMfaYrGYqf7nnjeYpOswJjpuwfK7S323oNeJVJ0SSau5erJ9ff/l\nmp1ivnJjT/ct569Y6mudE+ZPWTnl/x8+fBjVRR3L3zV1npyY/1yioZxPpoZ1XXpsaOYzxyimnovF\n7Kb6lGsXc19Ljlo+6vj7Asw8rkOnmLb6qdvPMen6Hufep/oZzcm45erqvpe0Y/195t+BBZZ6icrg\nVlRUVFRUVFRU3CrcGAZ3HEfzr/+5CP+SHUjunPr/2i8LO4a3337bl8WxL3/ZReSn0jQ2Tdg/oL1c\ndGGqf3q3BSY3d+055nbu3CV1fRvDmP1utaPbk2mWtS8S7qn2PYNPM1HYMb90zzG5v//7v+/qcPKG\nK1F2xYxtv2NftCNmrMAUs6pCI9hS+MYSKxJcKTF5fD56EJI3/MVf/QUREX3vj5zP7ScffURERHfu\ncJIFMVb2rJqA69BJD5CiVz6ucC8pQhj7aUZpiU8mUML2zkXflvjr5nb+wFykuZUGVLcPlQ6LnU0l\nS7HE91NWEM305ditlH+zhZTYvpxDpf7A1tiw/ATl7yXIrheKicn5o86xNtbz1s8slczGOmfJ+qTr\n6HttMeklfSmFNZ50XIcek1ZZWDBSqkGWn/ncemE9h9Szy73j51IzW2W01cJikOfOkyurj+cslZoN\nzzGh2kqnn5nF7qcSSFjQ7WpGN8eop77nsKTuHHO/hOVPoTK4FRUVFRUVFRUVtwo3isEFrLRx14Fm\nbTQ7IdvWkf34xO796ChE48JXMuX3E64lXJNuf5omML071u0f+ulu5jqacnP+OCWKDvBV9WWNKnNM\nsXzengkZ3G/Hx3EKXex41+vgw/rFL36JiIj+tX/0j6I2rKjSi3P37E7OXLvYBevUm3KsrNo4sv94\nE0dDf/Thz4mI6E/+5E98nbf//O3o2k5PXH/3PJ76Pvj1eXUGZopxjbillr9riiFJjd8crOdfGjW8\nZEddMpdLIpxT157Tj9X91wyc1X6qrkTKypIqZ50nV0aXTfXb0hZO1cnpiqbWqVwf9TGMZytSPjV+\nLMYNcQta51OzdJIZ1deau545i0bJc9epda17qhn0knVVM8X6mnNMsX5msk9YD/S4LWH0UuoculwO\nVvspBj33nLXSCZSLSpDyby55LiWYY7gl466ZZ5+i/uoq+t3q57NYsJZY77T12VpfgZL3Q+rcJe+H\nJX/fEFUGt6KioqKioqKi4pbhRjC4I7m/zEuYpEndgp1ISeR3ioXFJ7RuiUIGKzAMFxcXRBR280cc\nbS+vAz6jEx3DhnfSxl6jJBIYKN1hlvjE6O8lO9scgztXF2wtEVHPjPZqFfsl4v69+uqrRET0jW98\nw9f5jd/4DSIKWrbUxkyo3N23qzh71Ompe5brCesU7sUB2cDwG7MHH3zwARERvfOOU0r4wQ/e8XWG\nvWsf4wpjA1d6sgkM9OHgyp7vmO31LDLXKfD3mjtuIcfgp7IXATlGcm5O5hiAnCUCv+ksVSk/M/l/\n3adU3Vwd/TvRVEUhxS7nnqFuV2cssspqVrBEz1KvgxbbmHruORazhB3X/U0x39Z1INJc+5qijjyu\n/Vy1Dq5EisErGcc685dlCUA9PUb07zlfQ+2LaUXXp1Q4rAxqqWvSfc3px1r+xvr7nCUjZbGV/db3\nwMoAhmPB6tWYdeX/5zR6JUrXNPkbnlFqHbF0la0sZ6Xnuw5zXmJ5Ss196++zVB+s9XWuv7l3QGVw\nKyoqKioqKioqPtOof+BWVFRUVFRUVFTcKtwIFwUa41S9lqkU0PS1RaOnzDy6TVlHB+Nox3aYjYmm\nEivaQdwKJkDqWR3oFsxJ0+CB5DU2U9p/ScBKqkxJ0MDEnDeqsu3UFDF5hsTuB00sASbr+CAOvl9I\nEvA7v/M7RET01a9+1deBeedy6xzzfXACP7un50EmDP0+u+tkos45ze4pH8fztgIBLveu/Yv7T4mI\n6Dvf+b+JiOgv//IviSi4GhARbSDHw8ECXRebDXfb0Cdc/+rE1YE7BhJHWPcxZQKcmprNY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r9RHv3M1mE10PkjudnLhkT3JeaGmx3Bpdek/lvLiOi2c6wDiPyuBWVFRUVFRUVFTcKtwI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\nhEyK/wNZn0AYZpoQgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74d44e4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the predictions.\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.imshow(batch_images[i])\n",
    "\n",
    "current_axis = plt.gca()\n",
    "\n",
    "classes = ['background', 'car', 'truck', 'pedestrian', 'bicyclist',\n",
    "           'traffic_light', 'motorcycle', 'bus', 'stop_sign'] # Just so we can print class names onto the image instead of IDs\n",
    "\n",
    "# Draw the predicted boxes in blue\n",
    "for box in y_pred_thresh[i]:\n",
    "    class_id = box[0]\n",
    "    confidence = box[1]\n",
    "    xmin = box[2]\n",
    "    ymin = box[3]\n",
    "    xmax = box[4]\n",
    "    ymax = box[5]\n",
    "    label = '{}: {:.2f}'.format(classes[int(class_id)], confidence)\n",
    "    current_axis.add_patch(plt.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, color='blue', fill=False, linewidth=2))  \n",
    "    current_axis.text(xmin, ymin, label, size='x-large', color='white', bbox={'facecolor':'blue', 'alpha':1.0})\n",
    "\n",
    "# Draw the ground truth boxes in green (omit the label for more clarity)\n",
    "for box in batch_labels[i]:\n",
    "    class_id = box[0]\n",
    "    xmin = box[1]\n",
    "    ymin = box[2]\n",
    "    xmax = box[3]\n",
    "    ymax = box[4]\n",
    "    label = '{}'.format(classes[int(class_id)])\n",
    "    current_axis.add_patch(plt.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, color='green', fill=False, linewidth=2))  \n",
    "    #current_axis.text(box[1], box[3], label, size='x-large', color='white', bbox={'facecolor':'green', 'alpha':1.0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Seems as if our sub-sampled weights were doing a good job, sweet. Now we can fine-tune this model on our dataset with 8 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
